notebooks/2_preprocessing.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qx4zes1Ddutr"
      },
      "source": [
        "# **Text preprocessing and dictionary methods**\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sekhansen/text_algorithms_econ/blob/main/notebooks/2_preprocessing.ipynb)\n",
        "\n",
        "This notebook illustrates multiple strategies in order to transform raw text into a document-term matrix. As a starting point, we will use the built-in functions from the ```scikit-learn``` library to process our text and generate the document-term matrix. We will then refine this strategy by applying a more complex text preprocessing pipeline.\n",
        "<br> <br>\n",
        "\n",
        "The document-term matrix can be used as the input to many bag-of-words text analysis algorithms such as dictionary methods, Latent Semantic Analysis (LSA) or Linear Dirichlet Allocation (LDA). Through the notebook, we will focus on a concrete application of dictionary methods. We will show how the document-term matrix can be used to measure the sentiment of documents and how this sentiment is correlated with economic variables of interest."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m4R6QoDegWHo"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "doYuBxVGbxKU"
      },
      "source": [
        "# 0. Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "zQIQPtjWbyaE"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "\n",
        "# install required libraries\n",
        "!pip3 install flashtext                  # easy phrase replacing methods\n",
        "!pip3 install contractions               # expand English contractions \n",
        "!pip3 install --upgrade spacy==2.2.4     # functions for lemmatizing\n",
        "!pip3 install gdown                      # download files from Google Drive\n",
        "!pip3 install nltk                       # NLP library\n",
        "\n",
        "# install Spacy's language model\n",
        "# for more languages and models check: https://spacy.io/models\n",
        "!python3 -m spacy download en_core_web_sm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ocRzjkIxTwwf",
        "outputId": "effa9171-b09c-49fb-8eb8-474bfb542c1f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'text-preprocessing'...\n",
            "remote: Enumerating objects: 31, done.\u001b[K\n",
            "remote: Counting objects: 100% (31/31), done.\u001b[K\n",
            "remote: Compressing objects: 100% (24/24), done.\u001b[K\n",
            "remote: Total 31 (delta 11), reused 17 (delta 4), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (31/31), 1.95 MiB | 8.68 MiB/s, done.\n"
          ]
        }
      ],
      "source": [
        "# clone the GitHub repository with the preprocessing scripts\n",
        "!git clone https://github.com/unstructured-data-research/text-preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "pT89oKqcOUYz"
      },
      "outputs": [],
      "source": [
        "# import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import gdown\n",
        "import random\n",
        "import string\n",
        "import re\n",
        "import random\n",
        "import sys\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import contractions\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "\n",
        "import unicodedata\n",
        "import spacy\n",
        "nlp_standard = spacy.load('en_core_web_sm')\n",
        "\n",
        "import nltk\n",
        "from nltk import SnowballStemmer\n",
        "stemmer = SnowballStemmer(language='english')\n",
        "\n",
        "# import our own modules\n",
        "sys.path.append('./text-preprocessing/src')\n",
        "import preprocessing_class as pc\n",
        "import preprocess_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "oYSA60vTZgJn"
      },
      "outputs": [],
      "source": [
        "# define paths and seed\n",
        "seed = 92\n",
        "data_path = \"./\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "1ASFG1hzHeUP"
      },
      "outputs": [],
      "source": [
        "# define dictionary with paths to data in Google Drive\n",
        "data_url_dict = {\"mpc_minutes\":             (\"https://drive.google.com/uc?id=1k086m12gBHKuVMPv4GUD1aB_kZOZpznR\", \"txt\"),\n",
        "                 \"ons_quarterly_gdp\":       (\"https://drive.google.com/uc?id=1_8JwCNUsq9wnPiTDmcJ2xrWklGGDFbQD\", \"csv\"),\n",
        "                 \"gdp_sentiment\":           (\"https://drive.google.com/uc?id=1xxGecTJzbXLMgGE-2-YVKlMD2P0JYmgH\", \"csv\")\n",
        "                 }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VeM3yIZFICcl",
        "outputId": "5c6c60a2-bc50-46d4-94df-d7c9a4a83c53"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1k086m12gBHKuVMPv4GUD1aB_kZOZpznR\n",
            "To: /content/mpc_minutes.txt\n",
            "100%|██████████| 7.36M/7.36M [00:00<00:00, 53.3MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1_8JwCNUsq9wnPiTDmcJ2xrWklGGDFbQD\n",
            "To: /content/ons_quarterly_gdp.csv\n",
            "100%|██████████| 3.33k/3.33k [00:00<00:00, 13.4MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1xxGecTJzbXLMgGE-2-YVKlMD2P0JYmgH\n",
            "To: /content/gdp_sentiment.csv\n",
            "100%|██████████| 2.36k/2.36k [00:00<00:00, 1.85MB/s]\n"
          ]
        }
      ],
      "source": [
        "# download each file in the dictionary from Google Drive\n",
        "for file_name, attributes in data_url_dict.items():\n",
        "    url = attributes[0]\n",
        "    extension = attributes[1]\n",
        "    gdown.download(url, f\"{file_name}.{extension}\", quiet=False) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ftz2XnGwdutv"
      },
      "source": [
        "# 1. Load data\n",
        "\n",
        "This tutorial uses text data from the monetary policy discussions at the Bank of England. Concretely, we use the minutes from the monthly [Monetary Policy Committee](https://www.bankofengland.co.uk/about/people/monetary-policy-committee) meetings. You can browse through all of these minutes [here](https://www.bankofengland.co.uk/monetary-policy-summary-and-minutes/monetary-policy-summary-and-minutes) or explore one particular minute from September 2017 [here](https://www.bankofengland.co.uk/-/media/boe/files/monetary-policy-summary-and-minutes/2017/september-2017.pdf?la=en&hash=48D5F5B648234CFF1EE2AF02A05181107459E0A2)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9I32x0D2G0j"
      },
      "source": [
        "As a first step, we inspect the data. We observe that we have a total number of 7,277 strings of data (hereafter **documents**) with an associated date (YYYYMM) that ranges between 06/1997 and 10/2014. The average length of a document is roughly 161 words."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "WJomAJdvdutv",
        "outputId": "5efea90d-335a-4de8-e617-9c7abc4e120a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(7277, 2)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4545aad7-5899-44ef-b803-aa9d12e734c8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>minutes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>199706</td>\n",
              "      <td>1 Sections I to V of this minute summarise t...</td>\n",
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              "      <td>199706</td>\n",
              "      <td>The 12-month growth rate of notes and coins ...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>199706</td>\n",
              "      <td>Broad money, too, decelerated in April: its ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>199706</td>\n",
              "      <td>Lending growth continued at around 9% in ...</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>199706</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4545aad7-5899-44ef-b803-aa9d12e734c8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          const element = document.querySelector('#df-4545aad7-5899-44ef-b803-aa9d12e734c8');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     date                                            minutes\n",
              "0  199706    1 Sections I to V of this minute summarise t...\n",
              "1  199706    The 12-month growth rate of notes and coins ...\n",
              "2  199706    Broad money, too, decelerated in April: its ...\n",
              "3  199706       Lending growth continued at around 9% in ...\n",
              "4  199706       Lending to individuals remained robust in..."
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# load the data an inspect it\n",
        "data = pd.read_csv('mpc_minutes.txt', delimiter='\\t', header=0, names=['date', 'minutes'])\n",
        "print(data.shape)\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "DSKWV5q93dKa",
        "outputId": "75904ace-f93e-4ed0-e663-6b52e65ba5d6"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'       The Committee noted that the preliminary estimate of 0.2% GDP growth in 1998 Q4 was consistent with the degree of slowdown expected in its November central projection. Some members had feared lower growth, and the possibility of revisions remained. Surveys suggested that consumer and business sentiment had stopped deteriorating and had perhaps improved slightly, possibly helped by the 150 basis point reduction in official interest rates since October. The preliminary data on Q4 expenditure suggested, however, that household spending might have grown by less in relation to income and wealth than the Committee had assumed in November.'"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# start by reading a document. What are they?\n",
        "data.loc[929, \"minutes\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wMjQOLyj2PMk",
        "outputId": "c083b934-ca2a-4ce0-e816-74b15a3538a5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "160.6952040676103"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# calculate the average number of words in a document\n",
        "data[\"minutes\"].apply(lambda x: len(x.split())).mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ran5C7C2dutw",
        "outputId": "e566fe6f-bff2-4892-8d27-aaf87624464b"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>minutes</th>\n",
              "      <th>year</th>\n",
              "      <th>quarter</th>\n",
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              "      <th>0</th>\n",
              "      <td>199706</td>\n",
              "      <td>1 Sections I to V of this minute summarise t...</td>\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "     date                                            minutes  year  quarter\n",
              "0  199706    1 Sections I to V of this minute summarise t...  1997        2\n",
              "1  199706    The 12-month growth rate of notes and coins ...  1997        2\n",
              "2  199706    Broad money, too, decelerated in April: its ...  1997        2\n",
              "3  199706       Lending growth continued at around 9% in ...  1997        2\n",
              "4  199706       Lending to individuals remained robust in...  1997        2"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# turn date into date format and create quarter column\n",
        "data['date'] = pd.to_datetime(data.date, format='%Y%m')\n",
        "data['year'] = data.date.dt.year.astype(int)\n",
        "data['quarter'] = data.date.dt.quarter.astype(int)\n",
        "data['date'] = data['date'].dt.strftime('%Y%m')\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Piqhv31-dutx",
        "outputId": "dff763f9-e95b-47a3-950d-199578c786d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of months: 209\n",
            "number of years: 18\n"
          ]
        }
      ],
      "source": [
        "# inspect the coverage of our data\n",
        "print('number of months:', len(data.date.unique()))\n",
        "print('number of years:', len(data.year.unique()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5YJYlZkPdutx"
      },
      "source": [
        "#  2. Preprocessing text with scikit-learn\n",
        "\n",
        "We will use the ```CountVectorizer``` class from **scikit-learn** in order to both preprocess our text and transform it into a document-term matrix. Check the [documentation of the class](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html) to learn more about its parameters. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "MhuDNaD-fs57",
        "outputId": "fb930d54-150e-4db0-cbd5-4f593c02d721"
      },
      "outputs": [
        {
          "data": {
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              "                strip_accents=&#x27;ascii&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer(max_df=0.8, min_df=20, stop_words=&#x27;english&#x27;,\n",
              "                strip_accents=&#x27;ascii&#x27;)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "CountVectorizer(max_df=0.8, min_df=20, stop_words='english',\n",
              "                strip_accents='ascii')"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# create a CountVectorizer object straight from the raw text\n",
        "count_vectorizer = CountVectorizer(token_pattern=r\"(?u)\\b\\w\\w+\\b\",       # regular expression to split documents into tokens\n",
        "                                   lowercase=True,                       # convert all characters to lower-case\n",
        "                                   strip_accents=\"ascii\",                # remove non-ascii characters (WARNING: this is dangerous in Romance languages)\n",
        "                                   stop_words=\"english\",                 # remove stopwords from a built-in list. We can also provide our own list\n",
        "                                   ngram_range=(1, 1),                   # generate only unigrams\n",
        "                                   analyzer=\"word\",                      # build matrix at the word-level \n",
        "                                   max_df=0.8,                           # ignore tokens that have a higher document frequency (can be int or percent)\n",
        "                                   min_df=20,                            # ignore tokens that have a lower document frequency (can be int or percent)\n",
        "                                   max_features=None,                    # we could impose a maximum number of vocabulary terms\n",
        "                                   )                       \n",
        "count_vectorizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bb8r9nt4e2e4",
        "outputId": "b9d0ef93-0991-4e98-eecc-b1d369423e64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Document-term matrix created with shape: (7277, 2313)\n"
          ]
        }
      ],
      "source": [
        "# directly transform our data into a document-term matrix. We just need to pass\n",
        "# our documents as an argument to the fit_transform function\n",
        "dt_matrix_simple = count_vectorizer.fit_transform(data[\"minutes\"])\n",
        "print(f\"Document-term matrix created with shape: {dt_matrix_simple.shape}\")    # (documents, vocabulary size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tWhOANh6l_Nl",
        "outputId": "0498ce52-054e-486b-9747-bb5072973b63"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<7277x2313 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 384051 stored elements in Compressed Sparse Row format>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# inspect the document-term matrix. Why is it sparse?\n",
        "dt_matrix_simple"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "fd7GMIdamE8d"
      },
      "outputs": [],
      "source": [
        "# transform to regular numpy array to easily manipulate.\n",
        "# this might not be convenient (or even possible) if your matrix is too large\n",
        "dt_matrix_simple = dt_matrix_simple.toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQaKIZPPkFGP",
        "outputId": "d16363d3-1fbb-4aeb-d553-014df7d8c236"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('notable', 1414),\n",
              " ('300', 59),\n",
              " ('undertaken', 2180),\n",
              " ('recruit', 1729),\n",
              " ('access', 146),\n",
              " ('91', 105),\n",
              " ('revival', 1833),\n",
              " ('marian', 1301),\n",
              " ('mild', 1346),\n",
              " ('consumers', 529)]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# we can explore the vocaburaly as pairs of (word, position in matrix)\n",
        "vocabulary = count_vectorizer.vocabulary_\n",
        "random.sample(list(vocabulary.items()), 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sncQTkKnfH8C",
        "outputId": "2f1963c5-07f1-4045-ce71-8701707b2a99"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['000', '10', '100', ..., 'yields', 'yougov', 'zero'], dtype=object)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# another way of looking at the vocabulary (ordered by index in the document-term matrix)\n",
        "count_vectorizer.get_feature_names_out()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q4SFMif4kBCe"
      },
      "source": [
        "# 3. Tracking the sentiment towards the economy\n",
        "\n",
        "Consider a situation where we want to investigate the overall sentiment of each monetary policy meeting. This migth be a very interesting quantity since it reflects the perception that policy makers have of the current state of the economy.\n",
        "<br><br>\n",
        "\n",
        "A simple and sensible way to estimate this quantity would be to build a document-sentiment index based on the difference between the number of words having a \"positive\" sentiment and those having a \"negative\" sentiment in a document. **We will use our document-term matrix to do precisely this**. For each document $i$ we will calculate:\n",
        "<br><br>\n",
        "\n",
        "$$\n",
        "\\text{net sentiment}_i = \\frac{{\\text{# Positive terms}_i} - \\text{# Negative terms}_i}{{\\text{# Positive terms}_i} + \\text{# Negative terms}_i}\n",
        "$$\n",
        "\n",
        "<br><br>\n",
        "We will then evaluate such an index by comparing the measured sentiment with the UK's GDP growth.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z4SMXQmlGbDe"
      },
      "source": [
        "## Building dictionaries\n",
        "\n",
        "Firstly, we need to define the terms having positive or negative sentiment. For this example, we use a set of terms from [Apel and Blix-Grimaldi (2012)](http://archive.riksbank.se/Documents/Rapporter/Working_papers/2012/rap_wp261_120426.pdf) who analyzed the minutes from the Swedish Central Bank. In general, it is a good practice to build on dictionaries that have been already used in the literature."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "otKb5dLbdut7"
      },
      "outputs": [],
      "source": [
        "pos_words = [\"high\", \"higher\", \"highest\",\n",
        "             \"strong\", \"stronger\", \"strongest\",\n",
        "             \"increase\", \"increases\", \"increased\",\"increasing\",\n",
        "             \"fast\", \"faster\", \"fastest\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "qGCgPMoydut7"
      },
      "outputs": [],
      "source": [
        "neg_words = [\"low\", \"lower\", \"lowest\",\n",
        "             \"weak\", \"weaker\", \"weakest\",\n",
        "             \"decrease\", \"decreases\", \"decreased\", \"decreasing\",\n",
        "             \"slow\", \"slower\", \"slowest\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "HjpLK1_COUZo"
      },
      "outputs": [],
      "source": [
        "# find the position in the document-term matrix of each word from the dictionary\n",
        "pos_ixs = [v for k,v in vocabulary.items() if k in pos_words]\n",
        "neg_ixs = [v for k,v in vocabulary.items() if k in neg_words]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "9EL0dhLNOUZo"
      },
      "outputs": [],
      "source": [
        "# for each document, sum all the positive words\n",
        "pos_counts = np.take(dt_matrix_simple, pos_ixs, axis=1)\n",
        "pos_counts = pos_counts.sum(axis=1)\n",
        "\n",
        "# for each document, sum all the negative words\n",
        "neg_counts = np.take(dt_matrix_simple, neg_ixs, axis=1)\n",
        "neg_counts = neg_counts.sum(axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hqK4TT7FOUZp",
        "outputId": "e6b3adb0-cd46-4de4-e39e-f98630387839"
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            "text/plain": [
              "        date                                            minutes  year  \\\n",
              "0     199706    1 Sections I to V of this minute summarise t...  1997   \n",
              "1     199706    The 12-month growth rate of notes and coins ...  1997   \n",
              "2     199706    Broad money, too, decelerated in April: its ...  1997   \n",
              "3     199706       Lending growth continued at around 9% in ...  1997   \n",
              "4     199706       Lending to individuals remained robust in...  1997   \n",
              "...      ...                                                ...   ...   \n",
              "7272  201410        For most members, there remained insuffi...  2014   \n",
              "7273  201410        Set against this, the level of Bank Rate...  2014   \n",
              "7274  201410        For two members, economic circumstances ...  2014   \n",
              "7275  201410        The Governor invited the Committee to vo...  2014   \n",
              "7276  201410        The following members of the Committee w...  2014   \n",
              "\n",
              "      quarter  pos_counts_simple  neg_counts_simple  \n",
              "0           2                  0                  0  \n",
              "1           2                  0                  0  \n",
              "2           2                  2                  0  \n",
              "3           2                  1                  0  \n",
              "4           2                  1                  0  \n",
              "...       ...                ...                ...  \n",
              "7272        4                  4                  4  \n",
              "7273        4                  0                  4  \n",
              "7274        4                  1                  1  \n",
              "7275        4                  1                  0  \n",
              "7276        4                  0                  0  \n",
              "\n",
              "[7277 rows x 6 columns]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# add counts to dataframe\n",
        "data['pos_counts_simple'] = pos_counts\n",
        "data['neg_counts_simple'] = neg_counts\n",
        "data"
      ]
    },
    {
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      "execution_count": 23,
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              "\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   year  quarter  pos_counts_simple  neg_counts_simple\n",
              "0  1997        2                 27                 10\n",
              "1  1997        3                222                 72\n",
              "2  1997        4                229                 86\n",
              "3  1998        1                254                113\n",
              "4  1998        2                255                131"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# aggregate all minutes to the year-quarter level (by summing all the counts)\n",
        "data_agg = data.loc[:, ['year', 'quarter', 'pos_counts_simple', 'neg_counts_simple']].groupby(['year', 'quarter'], as_index=False).sum()\n",
        "data_agg.head()"
      ]
    },
    {
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      "execution_count": 24,
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        "id": "uFg6zwd5OUZq",
        "outputId": "4cb59631-e3a6-44a5-9021-047d702351c7"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>quarter</th>\n",
              "      <th>net_sentiment_simple</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1997</td>\n",
              "      <td>2</td>\n",
              "      <td>0.459459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1997</td>\n",
              "      <td>3</td>\n",
              "      <td>0.510204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1997</td>\n",
              "      <td>4</td>\n",
              "      <td>0.453968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>0.384196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1998</td>\n",
              "      <td>2</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   year  quarter  net_sentiment_simple\n",
              "0  1997        2              0.459459\n",
              "1  1997        3              0.510204\n",
              "2  1997        4              0.453968\n",
              "3  1998        1              0.384196\n",
              "4  1998        2              0.321244"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# compute net positive sentiment\n",
        "data_agg['net_sentiment_simple'] = (data_agg.pos_counts_simple - data_agg.neg_counts_simple)/(data_agg.pos_counts_simple + data_agg.neg_counts_simple)\n",
        "data_agg = data_agg[['year', 'quarter', 'net_sentiment_simple']].copy()\n",
        "data_agg.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpeVOrTIdut8"
      },
      "source": [
        "Next we add quarterly GDP data collected from the ONS website in order to compare our sentiment index with GDP growth."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
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        },
        "id": "s9SGNqqFOUZf",
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        {
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ],
            "text/plain": [
              "   year  quarter  gdp_growth\n",
              "0  1997        2         1.2\n",
              "1  1997        3         0.6\n",
              "2  1997        4         1.3\n",
              "3  1998        1         0.6\n",
              "4  1998        2         0.6"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# load GDP data\n",
        "df_gdp = pd.read_csv('ons_quarterly_gdp.csv', names=['label', 'gdp_growth', 'quarter_long'], header=0)\n",
        "df_gdp['year'] = df_gdp.label.apply(lambda x: x[:4]).astype(int)\n",
        "df_gdp['quarter'] = df_gdp.label.apply(lambda x: x[6]).astype(int)\n",
        "df_gdp = df_gdp[['year', 'quarter', 'gdp_growth']]\n",
        "df_gdp = df_gdp.drop_duplicates().reset_index(drop=True).copy()\n",
        "df_gdp.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NYjVsNUhdut9",
        "outputId": "dfa63520-10c7-4d42-99a7-36a45708a5dd"
      },
      "outputs": [
        {
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              "      <td>0.453968</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>0.384196</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1998</td>\n",
              "      <td>2</td>\n",
              "      <td>0.321244</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4906bd9-b6d2-438b-9f61-0503998dc020')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f4906bd9-b6d2-438b-9f61-0503998dc020 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f4906bd9-b6d2-438b-9f61-0503998dc020');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   year  quarter  net_sentiment_simple  gdp_growth\n",
              "0  1997        2              0.459459         1.2\n",
              "1  1997        3              0.510204         0.6\n",
              "2  1997        4              0.453968         1.3\n",
              "3  1998        1              0.384196         0.6\n",
              "4  1998        2              0.321244         0.6"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# merge both dataframes\n",
        "df = data_agg.merge(df_gdp, how='left', on=['year', 'quarter']).copy()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PCmxFbZydut9",
        "outputId": "45f13eac-895a-4e73-896c-bdd96396f98f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                      gdp_growth  net_sentiment_simple\n",
            "gdp_growth              1.000000              0.277038\n",
            "net_sentiment_simple    0.277038              1.000000\n"
          ]
        }
      ],
      "source": [
        "# check correlation betweeen both series\n",
        "print(df[['gdp_growth', 'net_sentiment_simple']].corr())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "c-jpqzYLOUZj",
        "outputId": "45922b8b-cc39-49a8-d2fa-079725c8964e"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot standardized series for growth and sentiment\n",
        "scaler = StandardScaler()\n",
        "ticks = np.arange(0, df.shape[0], 5)\n",
        "labs = np.array([str(x)+'Q'+str(y) for x,y in zip(df['year'],df['quarter'])])\n",
        "labs = labs[ticks]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,8))\n",
        "ax.plot(scaler.fit_transform(df.gdp_growth.values.reshape(-1, 1)).flatten(), \n",
        "        label='GDP', c='green', alpha=0.8)\n",
        "ax.plot(scaler.fit_transform(df[\"net_sentiment_simple\"].values.reshape(-1, 1)).flatten(), \n",
        "        label='sentiment index', linestyle='dashed', c='black')\n",
        "\n",
        "ax.set_xticks(ticks)\n",
        "ax.set_xticklabels(labs, rotation=60)\n",
        "ax.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U60Oghm8p_Hn"
      },
      "source": [
        "## Downsides of simple preprocessing\n",
        "\n",
        "Although ```scikit-learn``` makes it easy to preprocess text and transform it into a document-term matrix, we might want to go beyond the functionality provided by this library. For instance we may want to:\n",
        "+ Split contractions (e.g. don't --> do not)\n",
        "+ Remove all numbers\n",
        "+ Transform words into lemmas\n",
        "+ Transform words into stems\n",
        "+ **Read documents while we are transforming them**\n",
        "<br><br>\n",
        "\n",
        "In the next section, we will show how to implement these custom preprocessing steps."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwDI0xcogK2_"
      },
      "source": [
        "# 4. Custom preprocessing pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "huMvFCTvxTlD"
      },
      "source": [
        "## Basic cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "KXjDrF5pOUZj"
      },
      "outputs": [],
      "source": [
        "# lower case via the lower() built-in method\n",
        "docs = [s.lower() for s in data.minutes]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "FOlGu1hQOg4V"
      },
      "outputs": [],
      "source": [
        "# remove leading and trailing whitespaces\n",
        "docs = [d.strip() for d in docs]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "_Y8yP0_3OUZk",
        "outputId": "43e2c969-784e-4495-c8a9-315d2fbbaca6"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'We cannot increase the interest rate. They will not accept it. It is too risky.'"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# split contractions with the \"contractions\" library\n",
        "# the library mainly supports English but it provides the tools to extend its\n",
        "# functionality to other languages: https://github.com/kootenpv/contractions\n",
        "contractions.fix(\"We can't increase the interest rate. They won't accept it. It's too risky.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "embyhlpJEcDY"
      },
      "outputs": [],
      "source": [
        "# apply contractions function\n",
        "docs = list(map(lambda x: contractions.fix(x), docs))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "LFQfqPyROUZk"
      },
      "outputs": [],
      "source": [
        "# tokenize with the spaCy tokenizer\n",
        "def spacy_tokenizer_standard(sent):\n",
        "    sent = nlp_standard.tokenizer(sent)\n",
        "    tokens = [t.text for t in sent]\n",
        "    return tokens\n",
        "\n",
        "# apply function\n",
        "tokens = list(map(spacy_tokenizer_standard, docs))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "cuCvT681TdlE"
      },
      "outputs": [],
      "source": [
        "# we could use apply our own regular expression\n",
        "custom_pattern = r'''\n",
        "                 (?x)                # set flag to allow verbose regexps (to separate logical sections of pattern and add comments)\n",
        "                 \\w+(?:-\\w+)*        # word characters with internal hyphens\n",
        "                 | [][.,;\"'?():-_`]  # preserve punctuation as separate tokens\n",
        "                 '''\n",
        "\n",
        "tokens_custom = list(map(lambda x:nltk.regexp_tokenize(x, pattern=custom_pattern), docs))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ynWl_-1rFAsU",
        "outputId": "e6b10d32-bbb0-49f7-b504-bcbfea7a5f8b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  There was as yet no clear downtrend in net exports. Exports to non-EU countries had risen sharply: the USA had grown \n",
            "\n",
            "['there', 'was', 'as', 'yet', 'no', 'clear', 'downtrend', 'in', 'net', 'exports', '.', 'exports', 'to', 'non', '-', 'eu']\n",
            "['there', 'was', 'as', 'yet', 'no', 'clear', 'downtrend', 'in', 'net', 'exports', '.', 'exports', 'to', 'non-eu', 'countries', 'had']\n"
          ]
        }
      ],
      "source": [
        "# inspect a document to see the differences in the tokenization\n",
        "i = 10\n",
        "print(data.loc[i, \"minutes\"][0:118], \"\\n\")\n",
        "print(tokens[i][0:16])\n",
        "print(tokens_custom[i][0:16])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "5uliiKR_OUZk"
      },
      "outputs": [],
      "source": [
        "# remove non-ascii characters via unicodedata package\n",
        "def remove_non_ascii(tokens):\n",
        "    tokens = [unicodedata.normalize('NFKD', t).encode('ascii', 'ignore').decode('utf-8', 'ignore') for t in tokens]\n",
        "    return tokens\n",
        "    \n",
        "# apply function\n",
        "tokens = list(map(remove_non_ascii, tokens))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h01Eg5O3Fb3R",
        "outputId": "243595e2-5d25-4bbc-aa95-9966d3f38a29"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'a e u c'"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# BRIEF NOTE: Removing non-ascii characters from other languages is very dangerous.\n",
        "# You will loose a lot of information\n",
        "unicodedata.normalize('NFKD', \"à è ü ç\").encode('ascii', 'ignore').decode('utf-8', 'ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "yXS0TUGYOUZl"
      },
      "outputs": [],
      "source": [
        "# remove extra white spaces and short tokens via simple list comprehension\n",
        "def remove_short(tokens, min_length=2):\n",
        "    return [t.strip() for t in tokens if t.strip() != '' and len(t.strip()) > min_length] \n",
        "\n",
        "# apply function\n",
        "tokens = list(map(remove_short, tokens))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "PZTSgRlUOUZl",
        "outputId": "5237ed19-69bf-4df4-935c-ae575750ac15"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# remove punctuation using a regular expression. We can use this same \n",
        "# structure to remove any other character\n",
        "def remove_punctuation(tokens, punctuation):            \n",
        "    regex = re.compile('[%s]' % re.escape(punctuation))\n",
        "    tokens = [regex.sub('', t) for t in tokens]\n",
        "    return tokens\n",
        "\n",
        "# load a list of punctuation signs\n",
        "string.punctuation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "xjWWFpUhrLbw"
      },
      "outputs": [],
      "source": [
        "# apply punctuation removal function\n",
        "tokens = list(map(lambda x: remove_punctuation(x, string.punctuation), tokens))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_X5oZLqQOUZm",
        "outputId": "2464a7b5-435d-44f9-fd27-1fb186b53ab9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['another', 'had', 'go', 'but', 'my', 'like', 'and', 'make', 'put', 'herself']"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# remove stopwords via simple list comprehension\n",
        "def remove_stopwords(tokens, stopwords):\n",
        "    return [t for t in tokens if t not in stopwords]\n",
        "\n",
        "# load a custom list of stopwords\n",
        "custom_stopwords = list(preprocess_data.stp_long)\n",
        "custom_stopwords[0:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "9wVAyIBbrWGu"
      },
      "outputs": [],
      "source": [
        "# apply stopword removal function\n",
        "tokens = list(map(lambda x: \n",
        "                  remove_stopwords(x, stopwords=custom_stopwords),\n",
        "                  tokens))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "QDDzH7g6MNxt"
      },
      "outputs": [],
      "source": [
        "# remove numbers\n",
        "def remove_numbers(tokens, min_length=2):\n",
        "    translation_table = str.maketrans('', '', string.digits)\n",
        "    tokens = [t.translate(translation_table) for t in tokens if not t.isdigit()] \n",
        "    return  [t for t in tokens if t != '' and len(t) > min_length]\n",
        "\n",
        "# apply number removal function\n",
        "tokens = list(map(lambda x: \n",
        "                  remove_numbers(x, min_length=2),\n",
        "                  tokens))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXBOPuaYuSHo"
      },
      "source": [
        "## Stemming and lemmatization\n",
        "\n",
        "As a next step, one might attempt to group together words that are grammatically different but thematically identical. For example, it could be reasonable to keep words such as *'obligations'*, *'obligation'* and  *'oblige'* under a single token. Ultimately these three words denote the same concept, and so we might want them to share the same symbol. \n",
        "\n",
        "The two most popular techniques to achieve this goal are **stemming** and **lemmatization**.  \n",
        ">Stemming replaces each word with its root form. The resulting token is less readable by humans but encompases multiple words (e.g. *'tradition'* and *'traditional'* have the same stem: *'tradit'*). \n",
        "\n",
        ">Lemmatization replaces words with their dictionary form i.e. lemma. The words *'changing'*, *'changes'* and *'changed'* all have the same lemma: *'change'*.\n",
        "\n",
        "Below, we use both techniques (stemming [via nltk Snowball stemmer](https://www.geeksforgeeks.org/snowball-stemmer-nlp/) and lemmatization via [spaCy lemmatizer](https://spacy.io/api/lemmatizer)) and look at the different results on a specific document."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "1urSlwlCOUZn"
      },
      "outputs": [],
      "source": [
        "# stem via standard nltk Snowball Stemmer\n",
        "stemmer = SnowballStemmer(language='english')\n",
        "def stem(tokens):\n",
        "    \"\"\"stem all tokens that do not contain hyphens\"\"\"\n",
        "    return [stemmer.stem(t) if \"-\" not in t else t for t in tokens]\n",
        "\n",
        "# apply function\n",
        "stems = list(map(stem, tokens))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "askRHu7RvuMf",
        "outputId": "572f277f-f2fa-4bb4-869f-30e137d2d15c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  There was as yet no clear downtrend in net exports. Exports to non-EU countries had risen sharply: the USA had grown very fast in Q1, but it was expected by US  \n",
            "\n",
            "['yet', 'clear', 'downtrend', 'net', 'export', 'export', 'non', 'countri', 'risen', 'sharpli', 'usa', 'grown', 'fast', 'expect']\n"
          ]
        }
      ],
      "source": [
        "# inspect a document\n",
        "i = 10\n",
        "print(data.loc[i, \"minutes\"][0:162], \"\\n\")\n",
        "print(stems[i][0:14])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "6xN9jeCVOUZn"
      },
      "outputs": [],
      "source": [
        "# lemmatize with standard spaCy lemmatizer (takes a couple of minutes)\n",
        "nlp_standard = spacy.load('en_core_web_sm')\n",
        "def lemmatize_sent(sent):\n",
        "    sent = ' '.join(sent)\n",
        "    doc = nlp_standard(sent)\n",
        "    lemmas = [token.lemma_ if token.lemma_ != '-PRON-' else token.text for token in doc]\n",
        "    return lemmas\n",
        "\n",
        "# apply function\n",
        "lemmas = list(map(lemmatize_sent, tokens))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BVQ3nKKGwN0U",
        "outputId": "d39548a1-6171-43f4-d255-968563444286"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  There was as yet no clear downtrend in net exports. Exports to non-EU countries had risen sharply: the USA had grown very fast in Q1, but it was expected by US  \n",
            "\n",
            "Stems:  ['yet', 'clear', 'downtrend', 'net', 'export', 'export', 'non', 'countri', 'risen', 'sharpli', 'usa', 'grown', 'fast', 'expect'] \n",
            "\n",
            "Lemmas:  ['yet', 'clear', 'downtrend', 'net', 'export', 'export', 'non', 'country', 'rise', 'sharply', 'usa', 'grow', 'fast', 'expect']\n"
          ]
        }
      ],
      "source": [
        "# compare the same document\n",
        "i = 10\n",
        "print(data.loc[i, \"minutes\"][0:162], \"\\n\")\n",
        "print(\"Stems: \", stems[i][0:14], \"\\n\")\n",
        "print(\"Lemmas: \", lemmas[i][0:14])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Af1ZnvY-6W1N"
      },
      "source": [
        "## Restricting the vocabulary by looking at the term frequency\n",
        "\n",
        "Finally, one might want to drop very frequent terms, as they could be regarded as corpus-specific terms that do not add up much value. Similarly, one might want to drop very rare terms, considering that not much can be learnt from such infrequent items. \n",
        "\n",
        "To explore these ideas we will rank tokens according to pure document frequency (df) or term frequency inverse document frequency (tf-idf), where high weight is given to terms that appear frequently in the entire dataset, but in relatively few documents. In particular, the tf-idf score for term $v$ is computed according to the formula below:\n",
        "\n",
        "\\begin{equation}\n",
        "tfidf_v = (1+log(tf_v)) (log \\frac{N}{df_v+1})\n",
        "\\end{equation}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "E1sJYd027Dxj"
      },
      "outputs": [],
      "source": [
        "# simple auxiliary function to override the preprocessing done by sklearn\n",
        "def do_nothing(doc):\n",
        "    return doc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1gGEjwHV6W1O",
        "outputId": "7bd71cd0-7697-4e9e-a707-43e6e04e6165"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py:528: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[(3431, 'growth', 0),\n",
              " (3062, 'inflation', 1),\n",
              " (2641, 'rate', 2),\n",
              " (2489, 'committee', 3),\n",
              " (2388, 'prices', 4),\n",
              " (2294, 'market', 5),\n",
              " (2163, 'month', 6),\n",
              " (1941, 'demand', 7),\n",
              " (1905, 'remained', 8),\n",
              " (1862, 'output', 9)]"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# generate document frequency ranking for all terms in the vocabulary\n",
        "vectorizer = TfidfVectorizer(use_idf=False, norm=None, tokenizer=do_nothing, preprocessor=do_nothing) \n",
        "df_matrix = vectorizer.fit_transform(tokens).toarray()\n",
        "df_matrix_bool = np.where(df_matrix > 0, 1, 0)\n",
        "scores_df = df_matrix_bool.sum(axis=0)\n",
        "\n",
        "sorted_vocab = sorted(vectorizer.vocabulary_.items(),   key=lambda x: x[1])\n",
        "sorted_vocab_keys = list(np.array(sorted_vocab)[:,0])\n",
        "sorted_scores_df = sorted(set(scores_df), reverse=True)\n",
        "\n",
        "rank_dict = {k:val for k,val in zip(sorted_scores_df, list(range(len(sorted_scores_df))))}\n",
        "rank_tup = sorted(zip(scores_df, sorted_vocab_keys),  key=lambda x: x[0], reverse=True)\n",
        "df_ranking = [x + (rank_dict[x[0]],) for x in rank_tup]\n",
        "df_ranking[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-oNWr826W1O"
      },
      "source": [
        "As we can see, the token \"growth\" ranks first, with a document frequency of 3,431 out of 7,277 (i.e. \"growth\" is present in around 47% of the documents), and \"inflation\" and \"rate\" come right after. Let's now look at the top ranked tokens in terms of tf-df:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YhrnGOjw6W1P",
        "outputId": "a2a8eef1-b61a-45f1-9f75-95f9f4ee7c89"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(25.89188880741839, 'securitisations', 0),\n",
              " (25.517881250415055, 'ips', 1),\n",
              " (24.794963009728445, 'nfc', 2),\n",
              " (24.658359148064065, 'bcc', 3),\n",
              " (24.460899792318422, 'pnfcsa', 4),\n",
              " (24.163147658906983, 'annex', 5),\n",
              " (23.920156435028385, 'respectively', 6),\n",
              " (23.786282775779966, 'egarding', 7),\n",
              " (23.76832183070415, 'attacks', 8),\n",
              " (23.688367959279212, 'surplus', 9)]"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# function to compute term-frequency inverse document frequency\n",
        "def tf_idf_compute(term, num_docs, scores_tf, scores_df):\n",
        "    return (1+np.log(scores_tf[term])*np.log(num_docs/scores_df[term]))\n",
        "\n",
        "# generate term frequency inverse document frequency ranking for all terms in the vocabulary\n",
        "sorted_vocab = sorted(vectorizer.vocabulary_.items(),   key=lambda x: x[1])\n",
        "sorted_vocab = list(np.array(sorted_vocab)[:,0])\n",
        "scores_tf = df_matrix.sum(axis=0)\n",
        "scores_tfidf = [tf_idf_compute(t, len(tokens), scores_tf=scores_tf, scores_df=scores_df) for t in range(len(sorted_vocab))]\n",
        "sorted_scores_tfidf = sorted(set(scores_tfidf), reverse=True)\n",
        "\n",
        "rank_dict = {k:val for k,val in zip(sorted_scores_tfidf, list(range(len(sorted_scores_tfidf))))}\n",
        "rank_tup = sorted(zip(scores_tfidf, sorted_vocab),  key=lambda x: x[0], reverse=True)\n",
        "tfidf_ranking = [x + (rank_dict[x[0]],) for x in rank_tup]\n",
        "tfidf_ranking[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2WOqkIG6W1Q"
      },
      "source": [
        "The two methods do not agree. Indeed, tf-idf weights more those terms that are frequent, but in relatively few documents.\n",
        "<br><br>\n",
        "\n",
        "Let's now inspect the overall behavior of the document frequency ranking and the tf-idf ranking. As we can see, document frequency decays very fast (following a power-law), while tf-idf decreases at a slower pace."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "nyANfiui6W1R",
        "outputId": "f16260d8-5ce9-40f4-88ed-dedebcb14756"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot document frequency\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot([x[0] for x in df_ranking])\n",
        "plt.title('Document frequency ranking')\n",
        "plt.ylabel(\"Document frequency\")\n",
        "plt.xlabel(\"Term ranking\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "D7dGSGgN6W1S",
        "outputId": "8c35cf52-e47b-4cf9-9d4a-acef51ee7138"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# we can use a log-log scale to observe more clearly the power-law distribution (Zipf's law)\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.loglog([x[0] for x in df_ranking])\n",
        "plt.title('Document frequency ranking (log-log)')\n",
        "plt.ylabel(\"log document frequency\")\n",
        "plt.xlabel(\"log term ranking\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "PQX96wVY6W1T",
        "outputId": "87d30e21-7168-4d2b-b625-90e5a90206f9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot tf-ids ranking\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot([x[0] for x in tfidf_ranking])\n",
        "plt.title('Tf-idf ranking')\n",
        "plt.ylabel(\"tf-idf\")\n",
        "plt.xlabel(\"Term ranking\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9TzEpqugrum"
      },
      "source": [
        "Using the *tfidf* ranking, for instance, we can see that removing stems that have a score lower than the score of stems in positions around 5,800 might be reasonable; they are relatively uninformative and lots of them have a similar score.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHNHarRiia4j",
        "outputId": "93d805a0-4fd1-48fe-95f6-c5d241bf027f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "6.68334024544429"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# get the score of the stem in position 58000\n",
        "tfidf_ranking[5800][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "-6dKqcvtguCW"
      },
      "outputs": [],
      "source": [
        "# auxiliary function to easily remove undesired tokens\n",
        "def remove(tokens, to_remove):\n",
        "    if to_remove:\n",
        "        return [t for t in tokens if t not in to_remove]\n",
        "    else:\n",
        "        return tokens\n",
        "\n",
        "# function to remove tokens in documents according to their df or tfidf score \n",
        "def rank_remove(ranking, docs, min_cutoff_score=-np.inf, max_cutoff_score=np.inf):\n",
        "\n",
        "    # define list of tokens to remove\n",
        "    to_remove_low = set([t[1] for t in ranking if t[0] <= min_cutoff_score])\n",
        "    to_remove_high = set([t[1] for t in ranking if t[0] > max_cutoff_score])\n",
        "\n",
        "    # remove tokens\n",
        "    docs_clean = [remove(d, to_remove_low) for d in docs]\n",
        "    docs_clean = [remove(d, to_remove_high) for d in docs_clean]\n",
        "\n",
        "    return docs_clean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "eziN9wIEiRHv"
      },
      "outputs": [],
      "source": [
        "# update documents\n",
        "stems_clean = rank_remove(tfidf_ranking, stems, min_cutoff_score=tfidf_ranking[5800][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "tIBWaLGxjG-4"
      },
      "outputs": [],
      "source": [
        "# we can also remove stems that have a very high score\n",
        "stems_clean = rank_remove(tfidf_ranking, stems_clean, max_cutoff_score=20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJ5K1GbsxZIo"
      },
      "source": [
        "## Vectorization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8F6WJhRawl1i"
      },
      "source": [
        "Now that we have finished preprocessing our text we will use again ```scikit-learn``` to transform our list of tokens, lemmas or stems into a document-term matrix. Check the documentation for the [```CountVectorizer```](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html) and the [```TfidfVectorizer```](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer) to explore all the parameters and functionalities of these classes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "oPycQqzshBYo",
        "outputId": "1952373b-689d-4ca8-e8da-3c5307c5af95"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CountVectorizer(lowercase=False, min_df=0,\n",
              "                preprocessor=&lt;function do_nothing at 0x7fe75f4daf80&gt;,\n",
              "                tokenizer=&lt;function do_nothing at 0x7fe75f4daf80&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer(lowercase=False, min_df=0,\n",
              "                preprocessor=&lt;function do_nothing at 0x7fe75f4daf80&gt;,\n",
              "                tokenizer=&lt;function do_nothing at 0x7fe75f4daf80&gt;)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "CountVectorizer(lowercase=False, min_df=0,\n",
              "                preprocessor=<function do_nothing at 0x7fe75f4daf80>,\n",
              "                tokenizer=<function do_nothing at 0x7fe75f4daf80>)"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# create a CountVectorizer object straight from the raw text\n",
        "count_vectorizer = CountVectorizer(preprocessor=do_nothing,              # apply no additional preprocessing\n",
        "                                   tokenizer=do_nothing,                 # apply no additional tokenization\n",
        "                                   lowercase=False,                      # convert all characters to lower-case\n",
        "                                   strip_accents=None,                   # remove non-ascii characters\n",
        "                                   stop_words=None,                      # remove stopwords from a built-in list. We can also provide our own list\n",
        "                                   ngram_range=(1, 1),                   # generate only unigrams\n",
        "                                   analyzer='word',                      # analysis at the word-level \n",
        "                                   max_df=1.0,                           # ignore tokens that have a higher document frequency (can be int or percent)\n",
        "                                   min_df=0,                             # ignore tokens that have a lowe document frequency (can be int or percent)\n",
        "                                   max_features=None,                    # we could impose a maximum number of vocabulary terms\n",
        "                                   )                       \n",
        "count_vectorizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yxYH-NcIZ4jA",
        "outputId": "08f8da60-d01f-477e-8d20-0290db6e5590"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5048\n"
          ]
        }
      ],
      "source": [
        "# create the document term matrix using stems\n",
        "dt_matrix_custom = count_vectorizer.fit_transform(stems_clean).toarray()\n",
        "vocabulary_custom = count_vectorizer.vocabulary_\n",
        "print(len(vocabulary_custom))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "id": "otyxipz9TdlW",
        "outputId": "8ad307e2-fa8c-4fcb-8160-67e482675187"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer(lowercase=False, max_df=0.8, min_df=20,\n",
              "                preprocessor=&lt;function do_nothing at 0x7fe75f4daf80&gt;,\n",
              "                sublinear_tf=True,\n",
              "                tokenizer=&lt;function do_nothing at 0x7fe75f4daf80&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(lowercase=False, max_df=0.8, min_df=20,\n",
              "                preprocessor=&lt;function do_nothing at 0x7fe75f4daf80&gt;,\n",
              "                sublinear_tf=True,\n",
              "                tokenizer=&lt;function do_nothing at 0x7fe75f4daf80&gt;)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "TfidfVectorizer(lowercase=False, max_df=0.8, min_df=20,\n",
              "                preprocessor=<function do_nothing at 0x7fe75f4daf80>,\n",
              "                sublinear_tf=True,\n",
              "                tokenizer=<function do_nothing at 0x7fe75f4daf80>)"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# we can also create a weighted vectorizer object\n",
        "# however we will not use it in the analysis\n",
        "weighted_vectorizer = TfidfVectorizer(preprocessor=do_nothing,              # apply no additional preprocessing\n",
        "                                      tokenizer=do_nothing,                 # apply no additional tokenization\n",
        "                                      lowercase=False,                      # convert all characters to lower-case\n",
        "                                      strip_accents=None,                   # remove non-ascii characters\n",
        "                                      stop_words=None,                      # remove stopwords from a built-in list. We can also provide our own list\n",
        "                                      ngram_range=(1, 1),                   # generate only unigrams\n",
        "                                      analyzer='word',                      # analysis at the word-level \n",
        "                                      max_df=0.8,                           # ignore tokens that have a higher document frequency (can be int or percent)\n",
        "                                      min_df=20,                            # ignore tokens that have a lowe document frequency (can be int or percent)\n",
        "                                      max_features=None,                    # we could impose a maximum number of vocabulary terms\n",
        "                                      \n",
        "                                      use_idf=True,                         # wether to apply inverse document frequency weights\n",
        "                                      smooth_idf=True,                      # add +1 to idf weighting\n",
        "                                      sublinear_tf=True                     # add +1 to log(tf)\n",
        "                                   )                       \n",
        "weighted_vectorizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JIHNkkajxejm"
      },
      "source": [
        "## Dictionary methods"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "lQ3N9G6xhwdH"
      },
      "outputs": [],
      "source": [
        "# transform dictionary terms into stems (in order for them to match our new vocabulary)\n",
        "pos_stems = [stemmer.stem(t) for t in pos_words]\n",
        "pos_stems = list(set(pos_stems))\n",
        "neg_stems = [stemmer.stem(t) for t in neg_words]\n",
        "neg_stems = list(set(neg_stems))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "8BEhobieh7i4"
      },
      "outputs": [],
      "source": [
        "# find positions of the stems in the document-term matrix\n",
        "pos_ixs = [v for k,v in vocabulary_custom.items() if k in pos_stems]\n",
        "neg_ixs = [v for k,v in vocabulary_custom.items() if k in neg_stems]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "vsWSSjKEh7i5"
      },
      "outputs": [],
      "source": [
        "# count positive stems per document\n",
        "pos_counts_custom = np.take(dt_matrix_custom, pos_ixs, axis=1)\n",
        "pos_counts_custom = pos_counts_custom.sum(axis=1)\n",
        "\n",
        "# count negative stems per document\n",
        "neg_counts_custom = np.take(dt_matrix_custom, neg_ixs, axis=1)\n",
        "neg_counts_custom = neg_counts_custom.sum(axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "d4jQeWf7h7i6",
        "outputId": "9ea3cf94-c8f7-482a-a845-915cb215e9ec"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-56752e9d-9cc5-41c5-b8c5-119af810f0b8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>minutes</th>\n",
              "      <th>year</th>\n",
              "      <th>quarter</th>\n",
              "      <th>pos_counts_simple</th>\n",
              "      <th>neg_counts_simple</th>\n",
              "      <th>pos_counts_custom</th>\n",
              "      <th>neg_counts_custom</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>199706</td>\n",
              "      <td>1 Sections I to V of this minute summarise t...</td>\n",
              "      <td>1997</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>199706</td>\n",
              "      <td>The 12-month growth rate of notes and coins ...</td>\n",
              "      <td>1997</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>199706</td>\n",
              "      <td>Broad money, too, decelerated in April: its ...</td>\n",
              "      <td>1997</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>199706</td>\n",
              "      <td>Lending growth continued at around 9% in ...</td>\n",
              "      <td>1997</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>199706</td>\n",
              "      <td>Lending to individuals remained robust in...</td>\n",
              "      <td>1997</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7272</th>\n",
              "      <td>201410</td>\n",
              "      <td>For most members, there remained insuffi...</td>\n",
              "      <td>2014</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7273</th>\n",
              "      <td>201410</td>\n",
              "      <td>Set against this, the level of Bank Rate...</td>\n",
              "      <td>2014</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7274</th>\n",
              "      <td>201410</td>\n",
              "      <td>For two members, economic circumstances ...</td>\n",
              "      <td>2014</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7275</th>\n",
              "      <td>201410</td>\n",
              "      <td>The Governor invited the Committee to vo...</td>\n",
              "      <td>2014</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7276</th>\n",
              "      <td>201410</td>\n",
              "      <td>The following members of the Committee w...</td>\n",
              "      <td>2014</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7277 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56752e9d-9cc5-41c5-b8c5-119af810f0b8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-56752e9d-9cc5-41c5-b8c5-119af810f0b8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-56752e9d-9cc5-41c5-b8c5-119af810f0b8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        date                                            minutes  year  \\\n",
              "0     199706    1 Sections I to V of this minute summarise t...  1997   \n",
              "1     199706    The 12-month growth rate of notes and coins ...  1997   \n",
              "2     199706    Broad money, too, decelerated in April: its ...  1997   \n",
              "3     199706       Lending growth continued at around 9% in ...  1997   \n",
              "4     199706       Lending to individuals remained robust in...  1997   \n",
              "...      ...                                                ...   ...   \n",
              "7272  201410        For most members, there remained insuffi...  2014   \n",
              "7273  201410        Set against this, the level of Bank Rate...  2014   \n",
              "7274  201410        For two members, economic circumstances ...  2014   \n",
              "7275  201410        The Governor invited the Committee to vo...  2014   \n",
              "7276  201410        The following members of the Committee w...  2014   \n",
              "\n",
              "      quarter  pos_counts_simple  neg_counts_simple  pos_counts_custom  \\\n",
              "0           2                  0                  0                  0   \n",
              "1           2                  0                  0                  0   \n",
              "2           2                  2                  0                  2   \n",
              "3           2                  1                  0                  1   \n",
              "4           2                  1                  0                  1   \n",
              "...       ...                ...                ...                ...   \n",
              "7272        4                  4                  4                  4   \n",
              "7273        4                  0                  4                  0   \n",
              "7274        4                  1                  1                  1   \n",
              "7275        4                  1                  0                  1   \n",
              "7276        4                  0                  0                  0   \n",
              "\n",
              "      neg_counts_custom  \n",
              "0                     0  \n",
              "1                     0  \n",
              "2                     0  \n",
              "3                     0  \n",
              "4                     0  \n",
              "...                 ...  \n",
              "7272                  5  \n",
              "7273                  4  \n",
              "7274                  1  \n",
              "7275                  0  \n",
              "7276                  0  \n",
              "\n",
              "[7277 rows x 8 columns]"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# add counts to dataframe\n",
        "data['pos_counts_custom'] = pos_counts_custom\n",
        "data['neg_counts_custom'] = neg_counts_custom\n",
        "data"
      ]
    },
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    </div>\n",
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              "  "
            ],
            "text/plain": [
              "   year  quarter  pos_counts_simple  neg_counts_simple  pos_counts_custom  \\\n",
              "0  1997        2                 27                 10                 21   \n",
              "1  1997        3                222                 72                206   \n",
              "2  1997        4                229                 86                215   \n",
              "3  1998        1                254                113                234   \n",
              "4  1998        2                255                131                227   \n",
              "\n",
              "   neg_counts_custom  \n",
              "0                 10  \n",
              "1                 70  \n",
              "2                100  \n",
              "3                139  \n",
              "4                162  "
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# aggregate to year-quarter level\n",
        "data_agg = data.groupby(['year', 'quarter']).sum(numeric_only=True).reset_index()\n",
        "data_agg.head()"
      ]
    },
    {
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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            "text/plain": [
              "   year  quarter  net_sentiment_simple  net_sentiment_custom\n",
              "0  1997        2              0.459459              0.354839\n",
              "1  1997        3              0.510204              0.492754\n",
              "2  1997        4              0.453968              0.365079\n",
              "3  1998        1              0.384196              0.254692\n",
              "4  1998        2              0.321244              0.167095"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# compute net sentiment\n",
        "data_agg['net_sentiment_simple'] = (data_agg.pos_counts_simple - data_agg.neg_counts_simple)/(data_agg.pos_counts_simple + data_agg.neg_counts_simple)\n",
        "data_agg['net_sentiment_custom'] = (data_agg.pos_counts_custom - data_agg.neg_counts_custom)/(data_agg.pos_counts_custom + data_agg.neg_counts_custom)\n",
        "\n",
        "data_agg = data_agg[['year', 'quarter', 'net_sentiment_simple', 'net_sentiment_custom']].copy()\n",
        "data_agg.head()"
      ]
    },
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-86a7518f-0d10-4525-8c13-8dab847a1521 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-86a7518f-0d10-4525-8c13-8dab847a1521');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   year  quarter  net_sentiment_simple  net_sentiment_custom  gdp_growth\n",
              "0  1997        2              0.459459              0.354839         1.2\n",
              "1  1997        3              0.510204              0.492754         0.6\n",
              "2  1997        4              0.453968              0.365079         1.3\n",
              "3  1998        1              0.384196              0.254692         0.6\n",
              "4  1998        2              0.321244              0.167095         0.6"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# merge to GDP data\n",
        "df = data_agg.merge(df_gdp, how='left', on=['year', 'quarter']).copy()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "raWFdWnbj1Mr"
      },
      "source": [
        "Looking at correlations, we see that the new sentiment index is slightly more correlated with GDP than our initial measure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vNh0LWl9i-82",
        "outputId": "58203cdc-c4f4-41be-f2a9-e956093acb4f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                      gdp_growth  net_sentiment_simple  net_sentiment_custom\n",
            "gdp_growth              1.000000              0.277038              0.288730\n",
            "net_sentiment_simple    0.277038              1.000000              0.964843\n",
            "net_sentiment_custom    0.288730              0.964843              1.000000\n"
          ]
        }
      ],
      "source": [
        "print(df[['gdp_growth', 'net_sentiment_simple', \"net_sentiment_custom\"]].corr())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "9sexakdNj1Mt",
        "outputId": "98e9098e-ede3-4f1e-d570-e6a28ec2c549"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot time series\n",
        "scaler = StandardScaler()\n",
        "ticks = np.arange(0, df.shape[0], 5)\n",
        "labs = np.array([str(x)+'Q'+str(y) for x,y in zip(df['year'],df['quarter'])])\n",
        "labs = labs[ticks]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,8))\n",
        "ax.plot(scaler.fit_transform(df.gdp_growth.values.reshape(-1, 1)).flatten(), \n",
        "        label='GDP', c='green', alpha=0.8)\n",
        "\n",
        "ax.plot(scaler.fit_transform(df[\"net_sentiment_simple\"].values.reshape(-1, 1)).flatten(), \n",
        "        label='sentiment index (simple processing)', linestyle='dashed', c='black')\n",
        "\n",
        "ax.plot(scaler.fit_transform(df[\"net_sentiment_custom\"].values.reshape(-1, 1)).flatten(), \n",
        "        label='sentiment index (custom processing)', linestyle='dashed', c='orange')\n",
        "\n",
        "ax.set_xticks(ticks)\n",
        "ax.set_xticklabels(labs, rotation=60)\n",
        "ax.legend()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3.10.2 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.2"
    },
    "vscode": {
      "interpreter": {
        "hash": "e7a0c3d8bc4d2db51aea30c86dc747f02938c345e0d55043ee1f11015ff2b63a"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}