nlp/textblob.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T17:15:55.704510Z",
     "start_time": "2020-11-06T17:15:55.644040Z"
    }
   },
   "outputs": [],
   "source": [
    "from newspaper import Article\n",
    "from textblob import TextBlob, Word \n",
    "from textblob.wordnet import VERB\n",
    "import plotly.express as px "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T15:56:02.941561Z",
     "start_time": "2020-11-06T15:56:01.695286Z"
    }
   },
   "outputs": [],
   "source": [
    "url = 'https://towardsdatascience.com/how-to-learn-data-science-when-life-does-not-give-you-a-break-a26a6ea328fd'\n",
    "article = Article(url)\n",
    "article.download()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T15:56:03.637409Z",
     "start_time": "2020-11-06T15:56:03.515096Z"
    }
   },
   "outputs": [],
   "source": [
    "article.parse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T15:57:16.533392Z",
     "start_time": "2020-11-06T15:57:16.530716Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'How to Learn Data Science when Life does not Give You a Break\\n\\nI Struggled to Dedicate Time for Data Science. But Finding new Strategies Enables me to Boost my Learning Rate and Accomplish More\\n\\nWith Every Struggle, there is a Way\\n\\nWith the goal of getting into the data science field, you decide on what sets of skills you want to obtain. Now you just need to have the time to sit down to obtain those skills. Eventually, you will become a competitive candidate and get the job that you want.\\n\\nBut life does not let you sit down. It demands you to keep going and does your normal stuff while still expects you to get 2+ years of experience in your resume. You feel stuck and frustrated. You know that you need to learn and get some projects done but find it so difficult to stick with your commitment. So should you give it up?\\n\\nPhoto by Steve Halama on Unsplash\\n\\nI experienced the same things 2 months ago. As a mathematics student with 5 classes and a job, I found it extremely difficult to find the time for data science. When I was about to give up, I told myself to keep going. It is not about working harder; it is about working smarter. I just needed to switch my learning strategies.\\n\\nIt’s not about Working Harder. It’s about Working the System. - Evan Spiegel, CEO of Snapchat\\n\\nNow I manage to finish many fun data science projects, publish at least 2 articles per week, have new findings in NLP research while maintaining my 4.0 GPA. As I keep asking myself questions about how I can be more efficient in my work, I find the way. Since new approaches are working for me, I want to share them with the hope that you could adopt some for yourself to get those skills in a short amount of time and make your data science journey more enjoyable.\\n\\nFinish One Thing Before Moving on to another\\n\\nWhat I used to do\\n\\nSpend one hour every day to work on one project for weeks. By the time I finish, I feel like I haven’t achieved anything much.\\n\\nMy New Approach\\n\\nSet a small goal of what I want to complete and finish it in a short amount of time. Let’s say when I come back from school, I would tell myself: I want to finish my school work in 2 hours and get my NLP project done for 2 hours. Tomorrow I will write a short article for 2 hours before getting to school.\\n\\nKnowing that I can complete these things in a short amount of time makes me excited and be in the flow. I can concentrate for hours without getting distracted. Seeing my work accomplished makes me feel motivated and have the momentum to keep going.\\n\\nCreate an Isolated Environment\\n\\nWhat I used to Do\\n\\nOpen several browsers for different projects I want to finish and new articles I want to read. I felt distracted knowing that there were still many things that I needed to finish.\\n\\nMy New Approach\\n\\nOnce I get one thing done, I close all the files or browsers related to the previous work and move on to the next one. If the work hasn’t been done yet and I want to go back to the websites later, I just use One Tab to condense multiple browsers into one. Seeing my browsers clear prevents my mind from being distracted and clustered to focus on the work.\\n\\nLater when I want to come back to my work, I just open One Tab and can quickly access my own work.\\n\\nAnother small secret that I have been adopting: put my phone on the airplane mode and turn off any notifications on my MacBook. I will answer messages at the time of my choice. Any message can wait.\\n\\nMange Between Exploration and Exploitation\\n\\nWhat I used to do\\n\\nI knew learning new knowledge and getting updated with new technologies and techniques is important in the data science field. So I would spend a lot of time per day to explore different tools and read many articles without applying them. I felt exhausted and incompetent because I realized there were so many things I needed to learn but I hadn’t learned any of them yet\\n\\nMy New Approach\\n\\nWhenever I see interesting articles on Medium or from other websites, if I am doing something at that moment, I will skim some worthy articles, save them to Instapaper, and put them in a specific category (data science, programming, math, data viz, etc) so I can easily get access to them later. When I have a small free time (like on the bus, on the line, or in the lunchtime), I will pull out the articles I save in the app to read. Once I finish reading, I either delete them or save them if I want to get access or try out the codes later.\\n\\nBy creating my own reading space and habits, I am able to get myself updated with new knowledge while still get the work done.\\n\\nShare what I’ve Learned\\n\\nWhat I used to do\\n\\nSilently did the work every day. When it was time for me to show my work on my resume, I had nothing to show because I never shared or published my work after finishing it.\\n\\nMy New Approach\\n\\nBreak a big project into smaller projects. Push my small accomplishments on Github or share my shortcodes on Medium. Then I will share my projects and articles on LinkedIn, Twitter, or my website. I never leave any of my projects and small accomplishments unnoticed.\\n\\nIf you are hesitant to share your work because you think your work is not good enough, you will never be good enough. Once you share and receive the positive (or negative) feedback from others, you will feel more motivated to improve your skills to share better content.\\n\\nSwitch “I Need to Do” to “I Want to Do”\\n\\nWhat I used to do\\n\\n“Can I skip my project for today? No, I should not make an excuse because I have promised myself that I need to dedicate the time to data science, for one hour every day, no matter what”. That thought gave me the feeling of obligation and the project became like homework or a job. I did not look forward to tackling the project.\\n\\nMy New Approach\\n\\nTell myself I could skip my project, skip writing the article, skip my research if I want to. Nobody forces me to do it. That thought surprisingly switches my mindset about the work. I have the freedom to choose what I want to do and I still choose to work on my project. That means I really like this project! This will be fun. I immediately feel a boost in energy and enthusiasm to get my hands on the project.\\n\\nConclusion\\n\\nWhether you think you can or you can’t, you’re right Henry Ford\\n\\nThere are still many skills that I need to obtain to become a data scientist. But as I see my experiment with new approaches help me to obtain new skills efficiently and get more work done, I know that if I do not give up and have the courage to change the approaches that are not working, I will eventually achieve my vision.\\n\\nThank you for reading my article. I will be happy to hear your opinions on any of these strategies and what strategies are working for you. I like to write about basic mathematical concepts and play with different data science tools. Follow me on Medium to get updated about my latest articles. You could also connect with me on LinkedIn and Twitter.\\n\\nFind me on the Artist of Data Science Podcast or check out my other blogs on data science topics:'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = article.text\n",
    "text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T17:54:44.671746Z",
     "start_time": "2020-11-06T17:54:44.666331Z"
    }
   },
   "outputs": [],
   "source": [
    "blob = TextBlob('When I was about to give up, I told myself to keep going. It is not about working harder; it is about working smarter.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T18:04:23.929170Z",
     "start_time": "2020-11-06T18:04:23.919932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WordList(['When', 'I', 'was', 'about', 'to', 'give', 'up', 'I', 'told', 'myself', 'to', 'keep', 'going', 'It', 'is', 'not', 'about', 'working', 'harder', 'it', 'is', 'about', 'working', 'smarter'])"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob.words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T18:04:37.100632Z",
     "start_time": "2020-11-06T18:04:37.091174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'When'"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob.words[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part-of-speech Tagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:02:58.667687Z",
     "start_time": "2020-11-06T16:02:57.252510Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /home/user/nltk_data...\n",
      "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('averaged_perceptron_tagger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T18:06:21.304539Z",
     "start_time": "2020-11-06T18:06:21.292407Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('When', 'WRB'),\n",
       " ('I', 'PRP'),\n",
       " ('was', 'VBD'),\n",
       " ('about', 'RB'),\n",
       " ('to', 'TO'),\n",
       " ('give', 'VB'),\n",
       " ('up', 'RP'),\n",
       " ('I', 'PRP'),\n",
       " ('told', 'VBD'),\n",
       " ('myself', 'PRP'),\n",
       " ('to', 'TO'),\n",
       " ('keep', 'VB'),\n",
       " ('going', 'VBG'),\n",
       " ('It', 'PRP'),\n",
       " ('is', 'VBZ'),\n",
       " ('not', 'RB'),\n",
       " ('about', 'IN'),\n",
       " ('working', 'VBG'),\n",
       " ('harder', 'NN'),\n",
       " ('it', 'PRP'),\n",
       " ('is', 'VBZ'),\n",
       " ('about', 'IN'),\n",
       " ('working', 'VBG'),\n",
       " ('smarter', 'NN')]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob.tags "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Noun phrase extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:03:40.953415Z",
     "start_time": "2020-11-06T16:03:36.447270Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package brown to /home/user/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/brown.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('brown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:05:08.668743Z",
     "start_time": "2020-11-06T16:05:08.663320Z"
    }
   },
   "outputs": [],
   "source": [
    "blob = TextBlob('I just needed to switch my learning strategies.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:05:09.835984Z",
     "start_time": "2020-11-06T16:05:09.833103Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WordList(['learning strategies'])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob.noun_phrases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:10:17.853160Z",
     "start_time": "2020-11-06T16:10:17.849525Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment(polarity=0.15833333333333333, subjectivity=0.48333333333333334)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob = TextBlob('Knowing that I can complete these things in a short amount of time makes me excited and be in the flow.')\n",
    "\n",
    "blob.sentiment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:09:26.129386Z",
     "start_time": "2020-11-06T16:09:26.123725Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.1"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob.sentiment.polarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:20:44.989822Z",
     "start_time": "2020-11-06T16:20:44.983972Z"
    }
   },
   "outputs": [],
   "source": [
    "blob = TextBlob('Knowing that I can complete these things in a short amount of time makes me excited and be in the flow.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:20:45.582838Z",
     "start_time": "2020-11-06T16:20:45.573750Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WordList(['Knowing', 'that', 'I', 'can', 'complete', 'these', 'things', 'in', 'a', 'short', 'amount', 'of', 'time', 'makes', 'me', 'excited', 'and', 'be', 'in', 'the', 'flow'])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob.words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:20:46.874351Z",
     "start_time": "2020-11-06T16:20:46.867873Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "know\n",
      "that\n",
      "i\n",
      "can\n",
      "complete\n",
      "these\n",
      "things\n",
      "in\n",
      "a\n",
      "short\n",
      "amount\n",
      "of\n",
      "time\n",
      "make\n",
      "me\n",
      "excite\n",
      "and\n",
      "be\n",
      "in\n",
      "the\n",
      "flow\n"
     ]
    }
   ],
   "source": [
    "for word in blob.words:\n",
    "    w = Word(word.lower())\n",
    "    print(w.lemmatize('v'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:27:32.314443Z",
     "start_time": "2020-11-06T16:27:32.309044Z"
    }
   },
   "outputs": [],
   "source": [
    "word = Word('browser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:27:37.428358Z",
     "start_time": "2020-11-06T16:27:37.422427Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('browser.n.01'), Synset('browser.n.02')]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word.synsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:27:53.915500Z",
     "start_time": "2020-11-06T16:27:53.909596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a viewer who looks around casually without seeking anything in particular',\n",
       " 'a program used to view HTML documents']"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word.definitions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spelling Correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:30:27.244721Z",
     "start_time": "2020-11-06T16:30:27.239106Z"
    }
   },
   "outputs": [],
   "source": [
    "blob = TextBlob('I just neded to swicth my learnin strategies.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:30:27.612652Z",
     "start_time": "2020-11-06T16:30:27.599210Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextBlob(\"I just need to switch my learning strategics.\")"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob.correct()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T16:31:18.020762Z",
     "start_time": "2020-11-06T16:31:17.930764Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('learning', 0.25384615384615383),\n",
       " ('warning', 0.24615384615384617),\n",
       " ('margin', 0.2153846153846154),\n",
       " ('latin', 0.13076923076923078),\n",
       " ('martin', 0.05384615384615385),\n",
       " ('lain', 0.046153846153846156),\n",
       " ('earning', 0.038461538461538464),\n",
       " ('marin', 0.007692307692307693),\n",
       " ('darwin', 0.007692307692307693)]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Word('larnin').spellcheck()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word and Noun Phrase Frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T20:12:02.466813Z",
     "start_time": "2020-11-06T20:12:02.461012Z"
    }
   },
   "outputs": [],
   "source": [
    "blob = TextBlob(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T20:12:03.368576Z",
     "start_time": "2020-11-06T20:12:03.305407Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob.word_counts['i']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T20:12:03.912266Z",
     "start_time": "2020-11-06T20:12:03.781628Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "variable=count<br>index=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "count",
         "marker": {
          "color": "#636efa"
         },
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     },
     "metadata": {},
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   ],
   "source": [
    "frequency = pd.DataFrame.from_dict(blob.word_counts, orient='index', columns=['count'])\n",
    "plot = px.bar(frequency.sort_values(by='count', ascending=False)[:30])\n",
    "plot "
   ]
  },
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   "source": []
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