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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lab Session 3: Applying Traditional NLP Techniques to Environmental Communication Data\n",
+    "\n",
+    "## Objectives\n",
+    "\n",
+    "By the end of this lab, you will be able to:\n",
+    "\n",
+    "1. Apply traditional NLP techniques to the annotated Sierra Club press release data\n",
+    "2. Implement and compare different text classification methods\n",
+    "3. Conduct topic modeling on environmental communication texts\n",
+    "4. Analyze word co-occurrence patterns in press releases\n",
+    "5. Visualize and interpret the results of these analyses\n",
+    "\n",
+    "## Installation\n",
+    "\n",
+    "Before we begin, let's install the necessary packages for this lab. Run the following cell to install the required libraries:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install nlp4ss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup and Data Loading\n",
+    "\n",
+    "First, let's set up our environment and load the data from our previous lab sessions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:hyfi.joblib.joblib:initialized batcher with <hyfi.joblib.batcher.batcher.Batcher object at 0x177910e30>\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                             content        zero_shot_topic  \\\n",
+      "0  April 5, 2017\\n\\n\\nContact\\n Emily Pomilio (20...       Renewable Energy   \n",
+      "1  October 28, 2021\\n\\n\\nContact\\nShiloh Hernande...  Environmental Justice   \n",
+      "2  December 6, 2018\\n\\n\\nContact\\nCourtney Bourgo...  Environmental Justice   \n",
+      "3  Black households face disproportionately high ...  Environmental Justice   \n",
+      "4  February 24, 2020\\n\\n\\nContact\\nGabby Brown, g...           Conservation   \n",
+      "\n",
+      "  few_shot_sentiment                                         key_issues  \n",
+      "0           Positive  energy efficiency; electricity usage reduction...  \n",
+      "1           Positive         water quality; contamination; strip mining  \n",
+      "2           Negative  habitat destruction; environmental damage; flo...  \n",
+      "3           Negative  Energy burdens; Historic discrimination; Disin...  \n",
+      "4            Neutral  oil and gas drilling in the Arctic; thermal co...  \n",
+      "Number of rows: 100\n"
+     ]
+    }
+   ],
+   "source": [
+    "from hyfi import HyFI\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import classification_report\n",
+    "from gensim import corpora\n",
+    "from gensim.models import LdaModel\n",
+    "import nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
+    "\n",
+    "if HyFI.is_colab():\n",
+    "    HyFI.mount_google_drive()\n",
+    "    project_root = \"/content/drive/MyDrive/courses/nlp4ss\"\n",
+    "else:\n",
+    "    project_root = \"$HOME/workspace/courses/nlp4ss\"\n",
+    "\n",
+    "h = HyFI.initialize(\n",
+    "    project_name=\"nlp4ss\",\n",
+    "    project_root=project_root,\n",
+    "    logging_level=\"WARNING\",\n",
+    "    verbose=True,\n",
+    ")\n",
+    "\n",
+    "# Load the data from previous lab sessions\n",
+    "topics_df = pd.read_csv(\n",
+    "    h.project.workspace_dir / \"data/processed/sierra_club_topics.csv\"\n",
+    ")\n",
+    "sentiment_df = pd.read_csv(\n",
+    "    h.project.workspace_dir / \"data/processed/sierra_club_sentiment.csv\"\n",
+    ")\n",
+    "issues_df = pd.read_csv(\n",
+    "    h.project.workspace_dir / \"data/processed/sierra_club_key_issues.csv\"\n",
+    ")\n",
+    "\n",
+    "# Merge the dataframes\n",
+    "merged_df = pd.merge(topics_df, sentiment_df, on=\"content\")\n",
+    "merged_df = pd.merge(merged_df, issues_df, on=\"content\")\n",
+    "\n",
+    "print(merged_df.head())\n",
+    "print(f\"Number of rows: {len(merged_df)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Text Classification: Comparing Traditional Methods\n",
+    "\n",
+    "Let's compare the performance of traditional text classification methods (Naive Bayes and Logistic Regression) with the LLM-based zero-shot classification we performed in Lab 2.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Naive Bayes Classification Report:\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                       precision    recall  f1-score   support\n",
+      "\n",
+      "       Climate Change       0.00      0.00      0.00         4\n",
+      "         Conservation       0.00      0.00      0.00         4\n",
+      "Environmental Justice       0.30      1.00      0.46         6\n",
+      "     Renewable Energy       0.00      0.00      0.00         3\n",
+      "  Wildlife Protection       0.00      0.00      0.00         3\n",
+      "\n",
+      "             accuracy                           0.30        20\n",
+      "            macro avg       0.06      0.20      0.09        20\n",
+      "         weighted avg       0.09      0.30      0.14        20\n",
+      "\n",
+      "Logistic Regression Classification Report:\n",
+      "                       precision    recall  f1-score   support\n",
+      "\n",
+      "       Climate Change       0.00      0.00      0.00         4\n",
+      "         Conservation       0.00      0.00      0.00         4\n",
+      "Environmental Justice       0.35      1.00      0.52         6\n",
+      "     Renewable Energy       1.00      1.00      1.00         3\n",
+      "  Wildlife Protection       0.00      0.00      0.00         3\n",
+      "\n",
+      "             accuracy                           0.45        20\n",
+      "            macro avg       0.27      0.40      0.30        20\n",
+      "         weighted avg       0.26      0.45      0.31        20\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prepare the data\n",
+    "X = merged_df[\"content\"]\n",
+    "y = merged_df[\"zero_shot_topic\"]\n",
+    "\n",
+    "# Split the data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.2, random_state=42\n",
+    ")\n",
+    "\n",
+    "# Create TF-IDF vectors\n",
+    "tfidf_vectorizer = TfidfVectorizer(max_features=5000)\n",
+    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
+    "X_test_tfidf = tfidf_vectorizer.transform(X_test)\n",
+    "\n",
+    "# Train and evaluate Naive Bayes classifier\n",
+    "nb_classifier = MultinomialNB()\n",
+    "nb_classifier.fit(X_train_tfidf, y_train)\n",
+    "nb_predictions = nb_classifier.predict(X_test_tfidf)\n",
+    "print(\"Naive Bayes Classification Report:\")\n",
+    "print(classification_report(y_test, nb_predictions))\n",
+    "\n",
+    "# Train and evaluate Logistic Regression classifier\n",
+    "lr_classifier = LogisticRegression(random_state=42, max_iter=1000)\n",
+    "lr_classifier.fit(X_train_tfidf, y_train)\n",
+    "lr_predictions = lr_classifier.predict(X_test_tfidf)\n",
+    "print(\"Logistic Regression Classification Report:\")\n",
+    "print(classification_report(y_test, lr_predictions))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Topic Modeling with LDA\n",
+    "\n",
+    "Now, let's apply Latent Dirichlet Allocation (LDA) to discover latent topics in the press releases.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LDA Topics:\n",
+      "Topic 0: 0.012*\"de\" + 0.012*\"sierra\" + 0.012*\"club\" + 0.010*\"energy\" + 0.006*\"clean\" + 0.006*\"communities\" + 0.006*\"la\" + 0.005*\"health\" + 0.005*\"environmental\" + 0.005*\"gas\"\n",
+      "Topic 1: 0.012*\"club\" + 0.012*\"energy\" + 0.012*\"sierra\" + 0.007*\"clean\" + 0.006*\"environmental\" + 0.005*\"communities\" + 0.005*\"public\" + 0.005*\"protect\" + 0.005*\"coal\" + 0.004*\"gas\"\n",
+      "Topic 2: 0.015*\"de\" + 0.012*\"club\" + 0.011*\"sierra\" + 0.010*\"energy\" + 0.008*\"la\" + 0.008*\"clean\" + 0.006*\"communities\" + 0.006*\"climate\" + 0.006*\"new\" + 0.005*\"power\"\n",
+      "Topic 3: 0.018*\"sierra\" + 0.013*\"energy\" + 0.012*\"club\" + 0.009*\"clean\" + 0.008*\"communities\" + 0.007*\"environmental\" + 0.007*\"grassroots\" + 0.006*\"public\" + 0.006*\"new\" + 0.005*\"climate\"\n",
+      "Topic 4: 0.016*\"club\" + 0.015*\"sierra\" + 0.007*\"environmental\" + 0.007*\"clean\" + 0.006*\"energy\" + 0.006*\"public\" + 0.005*\"communities\" + 0.005*\"health\" + 0.005*\"wildlife\" + 0.005*\"climate\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "def preprocess_text(text):\n",
+    "    tokens = word_tokenize(text.lower())\n",
+    "    stop_words = set(stopwords.words(\"english\"))\n",
+    "    return [token for token in tokens if token.isalpha() and token not in stop_words]\n",
+    "\n",
+    "\n",
+    "# Preprocess the texts\n",
+    "processed_texts = merged_df[\"content\"].apply(preprocess_text)\n",
+    "\n",
+    "# Create a dictionary and corpus\n",
+    "dictionary = corpora.Dictionary(processed_texts)\n",
+    "corpus = [dictionary.doc2bow(text) for text in processed_texts]\n",
+    "\n",
+    "# Train the LDA model\n",
+    "lda_model = LdaModel(corpus=corpus, id2word=dictionary, num_topics=5, random_state=42)\n",
+    "\n",
+    "# Print the topics\n",
+    "print(\"LDA Topics:\")\n",
+    "for idx, topic in lda_model.print_topics(-1):\n",
+    "    print(f\"Topic {idx}: {topic}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LDA visualization saved as 'lda_visualization.html'\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
+       "\n",
+       "\n",
+       "<div id=\"ldavis_el8144763022158881119366775\" style=\"background-color:white;\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "\n",
+       "var ldavis_el8144763022158881119366775_data = {\"mdsDat\": {\"x\": [0.011423739842694848, 0.018871985426622512, -0.024914354996128322, -0.012577174111165813, 0.007195803837976769], \"y\": [0.011075831928530698, -0.013933463354531113, -0.0026195253683464538, -0.0016056440323501957, 0.007082800826697043], \"topics\": [1, 2, 3, 4, 5], \"cluster\": [1, 1, 1, 1, 1], \"Freq\": [29.091224041687454, 29.08332875604097, 21.999213370835967, 9.936823399847801, 9.889410431587804]}, \"tinfo\": {\"Term\": [\"de\", \"energy\", \"la\", \"club\", \"sierra\", \"gas\", \"el\", \"en\", \"protect\", \"communities\", \"clean\", \"coal\", \"environmental\", \"para\", \"public\", \"que\", \"grassroots\", \"wildlife\", \"los\", \"new\", \"power\", \"del\", \"today\", \"las\", \"health\", \"information\", \"action\", \"national\", \"air\", \"state\", \"jersey\", \"pjm\", \"mopr\", \"nj\", \"prices\", \"weatherization\", \"capacity\", \"artificially\", \"heat\", \"lantry\", \"murphy\", \"beyer\", 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0.28762703189775235, 0.21995008321592827, 0.3130058876534364, 0.08459618585228011, 0.09305580443750812, 0.4078783789777125, 0.24472702738662752, 0.1468362164319765, 0.1142059461137595, 0.08157567579554251, 0.20161626015942488, 0.4398900221660179, 0.1832875092358408, 0.0916437546179204, 0.0916437546179204, 0.18229164425064537, 0.36458328850129074, 0.18229164425064537, 0.18229164425064537, 0.7187046714924031, 0.35935233574620157, 0.488165600583474, 0.488165600583474, 0.26965674981625615, 0.3595423330883415, 0.2022425623621921, 0.06741418745406404, 0.10112128118109605, 0.6099202687754974, 0.12677215825437324, 0.6338607912718662, 0.12677215825437324, 0.06338607912718662, 0.06338607912718662, 0.41412751070655085, 0.41412751070655085, 0.41412751070655085, 0.5705487150625695, 0.20957501336794845, 0.6287250401038453, 0.20957501336794845, 0.31059282929470045, 0.2773150261559825, 0.19966681883230744, 0.14420381360111093, 0.06655560627743581, 0.11011422910189833, 0.66068537461139, 0.11011422910189833, 0.055057114550949166, 0.055057114550949166, 0.5300772735441962, 0.2891310702460508, 0.15568596090171966, 0.3780944764756049, 0.08896340622955409, 0.11120425778694261, 0.24519251667022524, 0.5721158722305255, 0.08173083889007507, 0.08173083889007507, 0.08173083889007507, 0.09591557909508579, 0.09591557909508579, 0.47957789547542895, 0.19183115819017157, 0.09591557909508579, 0.5371079997956447, 0.17903599993188157, 0.17903599993188157, 0.08951799996594079, 0.08951799996594079, 0.11774615890233757, 0.44154809588376587, 0.29436539725584393, 0.08830961917675317, 0.029436539725584392, 0.47312708815573795, 0.15770902938524597, 0.2628483823087433, 0.052569676461748656, 0.052569676461748656, 0.6770726353621992, 0.5269661196900755, 0.38013431084904475, 0.17739601172955421, 0.3041074486792358, 0.03801343108490447, 0.10136914955974526, 0.5281652302792187, 0.26408261513960934, 0.32840743906152486, 0.16420371953076243, 0.16420371953076243, 0.16420371953076243, 0.32840743906152486, 0.24983912830232985, 0.39557861981202225, 0.17696938254748365, 0.09368967311337369, 0.08327970943410995, 0.2169011733436712, 0.4338023466873424, 0.15016235077638776, 0.1084505866718356, 0.08342352820910431, 0.30292035931203337, 0.13463127080534815, 0.38706490356537593, 0.06731563540267407, 0.0841445442533426, 0.12869707214003778, 0.6434853607001888, 0.12869707214003778, 0.12869707214003778, 0.07274593320135002, 0.6547133988121502, 0.14549186640270004, 0.03637296660067501, 0.07274593320135002, 0.35717558902565993, 0.3466704246425523, 0.16808263012972233, 0.05252582191553823, 0.06303098629864587, 0.29357158367559666, 0.36402876375773985, 0.19962867689940572, 0.05871431673511933, 0.09394290677619094, 0.13703603650004076, 0.548144146000163, 0.2283933941667346, 0.04567867883334692, 0.09135735766669384, 0.44706285569765364, 0.11176571392441341, 0.22353142784882682, 0.055882856962206705, 0.1676485708866201, 0.18367747371085258, 0.18367747371085258, 0.5510324211325577, 0.18367747371085258, 0.33389297403462875, 0.14309698887198377, 0.4054414684706207, 0.04769899629066125, 0.07154849443599189, 0.5285277869478382], \"Term\": [\"access\", \"access\", \"access\", \"access\", \"access\", \"action\", \"action\", \"action\", \"action\", \"action\", \"activistas\", \"addition\", \"addition\", \"addition\", \"addition\", \"addition\", \"administraci\\u00f3n\", \"administraci\\u00f3n\", \"administraci\\u00f3n\", \"administraci\\u00f3n\", \"administraci\\u00f3n\", \"administrator\", \"administrator\", \"ads\", \"agree\", \"agree\", \"agree\", \"air\", \"air\", \"air\", \"air\", \"air\", \"al\", \"al\", \"al\", \"al\", \"al\", \"aligns\", \"altos\", \"altos\", \"america\", \"america\", \"america\", \"america\", \"america\", \"angie\", \"angie\", \"angie\", \"arctic\", \"arctic\", \"arctic\", \"arctic\", \"arctic\", \"arizona\", \"arizona\", \"arizona\", \"arizona\", \"arizona\", \"arkansas\", \"arkansas\", \"arkansas\", \"arkansas\", \"artificially\", \"assassination\", \"assassination\", \"assassination\", \"associations\", \"bailout\", \"bernhardt\", \"bernhardt\", \"bernhardt\", \"bernhardt\", \"bernhardt\", \"beyer\", \"blanca\", \"blanca\", \"border\", \"border\", \"border\", \"border\", \"border\", \"bosques\", \"bosques\", \"canyons\", \"canyons\", \"canyons\", \"canyons\", \"capacity\", \"capacity\", \"capacity\", \"capacity\", \"capacity\", \"captive\", \"captive\", \"captive\", \"casa\", \"casa\", \"chickasaw\", \"chickasaw\", \"chickasaw\", \"chickasaw\", \"city\", \"city\", \"city\", \"city\", \"city\", \"civiles\", \"clean\", \"clean\", \"clean\", \"clean\", \"clean\", \"climate\", \"climate\", \"climate\", \"climate\", \"climate\", \"club\", \"club\", \"club\", \"club\", \"club\", \"coal\", \"coal\", \"coal\", \"coal\", \"coal\", \"colleague\", \"colleague\", \"commission\", \"commission\", \"commission\", \"commission\", \"commission\", \"communities\", \"communities\", \"communities\", \"communities\", \"communities\", \"community\", \"community\", \"community\", \"community\", \"community\", \"como\", \"como\", \"como\", \"como\", \"como\", \"comunidades\", \"comunidades\", \"comunidades\", \"comunidades\", \"comunidades\", \"con\", \"con\", \"con\", \"con\", \"con\", \"consumer\", \"contact\", \"contact\", \"contact\", \"contact\", \"contact\", \"continued\", \"continued\", \"continued\", \"continued\", \"continued\", \"corte\", \"corte\", \"corte\", \"corte\", \"court\", \"court\", \"court\", \"court\", \"court\", \"craig\", \"craig\", \"craig\", \"customers\", \"customers\", \"customers\", \"customers\", \"customers\", \"cuts\", \"da\\u00f1inas\", \"da\\u00f1inas\", \"dci\", \"dci\", \"de\", \"de\", \"de\", \"de\", \"de\", \"defender\", \"defender\", \"del\", \"del\", \"del\", \"del\", \"del\", \"demand\", \"demand\", \"demand\", \"demand\", \"demand\", \"denton\", \"denton\", \"denton\", \"denton\", \"deq\", \"deq\", \"deq\", \"deq\", \"deq\", \"different\", \"different\", \"different\", \"directions\", \"directions\", \"education\", \"education\", \"education\", \"education\", \"education\", \"efficiency\", \"efficiency\", \"efficiency\", \"efficiency\", \"efficiency\", \"ejecutivos\", \"el\", \"el\", \"el\", \"el\", \"el\", \"electric\", \"electric\", \"electric\", \"electric\", \"electric\", \"emp\", \"en\", \"en\", \"en\", \"en\", \"en\", \"energy\", \"energy\", \"energy\", \"energy\", \"energy\", \"energ\\u00eda\", \"energ\\u00eda\", \"energ\\u00eda\", \"english\", \"english\", \"english\", \"english\", \"english\", \"environmental\", \"environmental\", \"environmental\", \"environmental\", \"environmental\", \"epa\", \"epa\", \"epa\", \"epa\", \"epa\", \"es\", \"es\", \"es\", \"es\", \"es\", \"estados\", \"estados\", \"estados\", \"estados\", \"estados\", \"este\", \"este\", \"este\", \"este\", \"este\", \"est\\u00e1\", \"est\\u00e1\", \"est\\u00e1\", \"est\\u00e1\", \"est\\u00e1\", \"evergy\", \"evergy\", \"evergy\", \"evergy\", \"evergy\", \"every\", \"every\", \"every\", \"every\", \"every\", \"expandir\", \"expandir\", \"experiencia\", \"financiera\", \"financiera\", \"fomb\", \"fomb\", \"fondos\", \"fondos\", \"fossil\", \"fossil\", \"fossil\", \"fossil\", \"fossil\", \"frontera\", \"frontera\", \"fuel\", \"fuel\", \"fuel\", \"fuel\", \"fuel\", \"garett\", \"garland\", \"garland\", \"garland\", \"gas\", \"gas\", \"gas\", \"gas\", \"gas\", \"get\", \"get\", \"get\", \"get\", \"get\", \"gibbons\", \"gibbons\", \"gibbons\", \"girls\", \"girls\", \"girls\", \"girls\", \"girls\", \"govern\", \"govern\", \"grande\", \"grande\", \"grande\", \"grande\", \"grande\", \"grassroots\", \"grassroots\", \"grassroots\", \"grassroots\", \"grassroots\", \"gray\", \"gray\", \"gray\", \"gray\", \"grid\", \"grid\", \"grid\", \"grid\", \"grid\", \"ha\", \"ha\", \"ha\", \"ha\", \"ha\", \"health\", \"health\", \"health\", \"health\", \"health\", \"heat\", \"heat\", \"heat\", \"heat\", \"heat\", \"hunters\", \"hunters\", \"hunters\", \"identifying\", \"impossible\", \"incendios\", \"incendios\", \"incendios\", \"industria\", \"industria\", \"industria\", \"influential\", \"influential\", \"influential\", \"influential\", \"influential\", \"information\", \"information\", \"information\", \"information\", \"information\", \"infraestructura\", \"infraestructura\", \"infraestructura\", \"infraestructura\", \"jealous\", \"jersey\", \"jersey\", \"jersey\", \"junta\", \"junta\", \"junta\", \"justicia\", \"justicia\", \"justin\", \"justin\", \"kansas\", \"kansas\", \"kansas\", \"kansas\", \"kansas\", \"king\", \"king\", \"king\", \"king\", \"king\", \"la\", \"la\", \"la\", \"la\", \"la\", \"lantry\", \"lantry\", \"largest\", \"largest\", \"largest\", \"largest\", \"largest\", \"las\", \"las\", \"las\", \"las\", \"las\", \"latino\", \"latino\", \"latino\", \"latino\", \"lauren\", \"lauren\", \"lauren\", \"legal\", \"legal\", \"legal\", \"legal\", \"legal\", \"llc\", \"llc\", \"llc\", \"llc\", \"llc\", \"lng\", \"lng\", \"lng\", \"lng\", \"lng\", \"lobbying\", \"lobbying\", \"lobbying\", \"lobbying\", \"lobbying\", \"logging\", \"logging\", \"logging\", \"los\", \"los\", \"los\", \"los\", \"los\", \"lost\", \"mandates\", \"market\", \"market\", \"market\", \"market\", \"market\", \"markets\", \"markets\", \"markets\", \"markets\", \"markets\", \"mast\", \"medio\", \"medio\", \"medio\", \"medio\", \"members\", \"members\", \"members\", \"members\", \"members\", \"memphis\", \"memphis\", \"memphis\", \"memphis\", \"metano\", \"metano\", \"million\", \"million\", \"million\", \"million\", \"million\", \"missoula\", \"missoula\", \"missoula\", \"missoula\", \"monopoly\", \"monopoly\", \"monopoly\", \"monopoly\", \"mopr\", \"mopr\", \"mopr\", \"mountains\", \"mountains\", \"muro\", \"muro\", \"muro\", \"muro\", \"muro\", \"murphy\", \"m\\u00e1s\", \"m\\u00e1s\", \"m\\u00e1s\", \"m\\u00e1s\", \"m\\u00e1s\", \"nafta\", \"nafta\", \"national\", \"national\", \"national\", \"national\", \"national\", \"new\", \"new\", \"new\", \"new\", \"new\", \"nj\", \"njbpu\", \"nomination\", \"nomination\", \"nomination\", \"northern\", \"northern\", \"northern\", \"northern\", \"northern\", \"obama\", \"obama\", \"offset\", \"offshore\", \"offshore\", \"offshore\", \"offshore\", \"offshore\", \"opting\", \"organization\", \"organization\", \"organization\", \"organization\", \"organization\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"para\", \"para\", \"para\", \"para\", \"para\", \"parajes\", \"parajes\", \"parajes\", \"parajes\", \"pa\\u00eds\", \"pa\\u00eds\", \"pa\\u00eds\", \"pa\\u00eds\", \"pa\\u00eds\", \"people\", \"people\", \"people\", \"people\", \"people\", \"permit\", \"permit\", \"permit\", \"permit\", \"permit\", \"peterson\", \"peterson\", \"peterson\", \"petr\\u00f3leo\", \"petr\\u00f3leo\", \"petr\\u00f3leo\", \"petr\\u00f3leo\", \"pipeline\", \"pipeline\", \"pipeline\", \"pipeline\", \"pipeline\", \"pjm\", \"pjm\", \"pjm\", \"pjm\", \"places\", \"places\", \"places\", \"places\", \"places\", \"plan\", \"plan\", \"plan\", \"plan\", \"plan\", \"plants\", \"plants\", \"plants\", \"plants\", \"plants\", \"poder\", \"poder\", \"poder\", \"poder\", \"poder\", \"pollution\", \"pollution\", \"pollution\", \"pollution\", \"pollution\", \"por\", \"por\", \"por\", \"por\", \"por\", \"power\", \"power\", \"power\", \"power\", \"power\", \"press\", \"press\", \"press\", \"press\", \"press\", \"prices\", \"prices\", \"prices\", \"prices\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"proteger\", \"proteger\", \"proteger\", \"proteger\", \"proteger\", \"public\", \"public\", \"public\", \"public\", \"public\", \"puerto\", \"puerto\", \"puerto\", \"puerto\", \"puerto\", \"p\\u00fablica\", \"p\\u00fablica\", \"p\\u00fablica\", \"p\\u00fablica\", \"p\\u00fablica\", \"que\", \"que\", \"que\", \"que\", \"que\", \"radium\", \"radium\", \"radium\", \"radium\", \"radium\", \"reducci\\u00f3n\", \"refuge\", \"refuge\", \"refuge\", \"refuge\", \"refuge\", \"regressive\", \"regulaciones\", \"related\", \"related\", \"related\", \"related\", \"related\", \"released\", \"released\", \"released\", \"released\", \"released\", \"remaining\", \"remaining\", \"remaining\", \"remaining\", \"remaining\", \"renewable\", \"renewable\", \"renewable\", \"renewable\", \"renewable\", \"report\", \"report\", \"report\", \"report\", \"report\", \"reppenhagen\", \"reputaci\\u00f3n\", \"reputaci\\u00f3n\", \"reputations\", \"reputations\", \"resources\", \"resources\", \"resources\", \"resources\", \"resources\", \"response\", \"response\", \"response\", \"response\", \"response\", \"rico\", \"rico\", \"rico\", \"rico\", \"rico\", \"rio\", \"rio\", \"rio\", \"rio\", \"rio\", \"roadless\", \"roadless\", \"roadless\", \"roadless\", \"roadless\", \"rockies\", \"rockies\", \"rockies\", \"rockies\", \"rockies\", \"rocky\", \"rocky\", \"rocky\", \"said\", \"said\", \"said\", \"said\", \"said\", \"salmon\", \"salmon\", \"salud\", \"salud\", \"salud\", \"salud\", \"salud\", \"scheme\", \"se\", \"se\", \"se\", \"se\", \"se\", \"senate\", \"senate\", \"senate\", \"senate\", \"senate\", \"service\", \"service\", \"service\", \"service\", \"service\", \"sierra\", \"sierra\", \"sierra\", \"sierra\", \"sierra\", \"sobre\", \"sobre\", \"sobre\", \"sobre\", \"sobre\", \"solar\", \"solar\", \"solar\", \"solar\", \"solar\", \"species\", \"species\", \"species\", \"species\", \"species\", \"srp\", \"srp\", \"srp\", \"state\", \"state\", \"state\", \"state\", \"state\", \"statement\", \"statement\", \"statement\", \"statement\", \"statement\", \"states\", \"states\", \"states\", \"states\", \"states\", \"strategic\", \"strategic\", \"strategic\", \"strategic\", \"summit\", \"summit\", \"supervisi\\u00f3n\", \"supervisi\\u00f3n\", \"supporters\", \"supporters\", \"supporters\", \"supporters\", \"supporters\", \"suprema\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"terrible\", \"terrible\", \"terrible\", \"tilt\", \"timber\", \"timber\", \"timber\", \"today\", \"today\", \"today\", \"today\", \"today\", \"tongass\", \"tongass\", \"tongass\", \"tongass\", \"tongass\", \"transformative\", \"transition\", \"transition\", \"transition\", \"transition\", \"transition\", \"trees\", \"trees\", \"trees\", \"trees\", \"trees\", \"un\", \"un\", \"un\", \"un\", \"un\", \"usps\", \"usps\", \"usps\", \"usps\", \"usps\", \"utah\", \"utah\", \"utah\", \"utah\", \"utah\", \"vehicles\", \"vehicles\", \"vehicles\", \"vehicles\", \"vehicles\", \"vet\", \"veteran\", \"water\", \"water\", \"water\", \"water\", \"water\", \"weatherization\", \"weatherization\", \"wholesale\", \"wholesale\", \"wholesale\", \"wholesale\", \"wholesale\", \"wild\", \"wild\", \"wild\", \"wild\", \"wild\", \"wildlife\", \"wildlife\", \"wildlife\", \"wildlife\", \"wildlife\", \"wind\", \"wind\", \"wind\", \"wind\", \"wind\", \"wolf\", \"wolf\", \"wolf\", \"wolf\", \"wolves\", \"wolves\", \"wolves\", \"wolves\", \"wolves\", \"works\", \"works\", \"works\", \"works\", \"works\", \"would\", \"would\", \"would\", \"would\", \"would\", \"wyoming\", \"wyoming\", \"wyoming\", \"wyoming\", \"wyoming\", \"xcel\", \"xcel\", \"xcel\", \"xcel\", \"xcel\", \"ya\", \"ya\", \"ya\", \"ya\", \"york\", \"york\", \"york\", \"york\", \"york\", \"zeldin\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [4, 5, 3, 1, 2]};\n",
+       "\n",
+       "function LDAvis_load_lib(url, callback){\n",
+       "  var s = document.createElement('script');\n",
+       "  s.src = url;\n",
+       "  s.async = true;\n",
+       "  s.onreadystatechange = s.onload = callback;\n",
+       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
+       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "}\n",
+       "\n",
+       "if(typeof(LDAvis) !== \"undefined\"){\n",
+       "   // already loaded: just create the visualization\n",
+       "   !function(LDAvis){\n",
+       "       new LDAvis(\"#\" + \"ldavis_el8144763022158881119366775\", ldavis_el8144763022158881119366775_data);\n",
+       "   }(LDAvis);\n",
+       "}else if(typeof define === \"function\" && define.amd){\n",
+       "   // require.js is available: use it to load d3/LDAvis\n",
+       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
+       "   require([\"d3\"], function(d3){\n",
+       "      window.d3 = d3;\n",
+       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
+       "        new LDAvis(\"#\" + \"ldavis_el8144763022158881119366775\", ldavis_el8144763022158881119366775_data);\n",
+       "      });\n",
+       "    });\n",
+       "}else{\n",
+       "    // require.js not available: dynamically load d3 & LDAvis\n",
+       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
+       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
+       "                 new LDAvis(\"#\" + \"ldavis_el8144763022158881119366775\", ldavis_el8144763022158881119366775_data);\n",
+       "            })\n",
+       "         });\n",
+       "}\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Visualize the topics\n",
+    "import pyLDAvis.gensim\n",
+    "\n",
+    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, dictionary)\n",
+    "pyLDAvis.save_html(vis, \"lda_visualization.html\")\n",
+    "print(\"LDA visualization saved as 'lda_visualization.html'\")\n",
+    "pyLDAvis.display(vis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Word Co-occurrence Analysis\n",
+    "\n",
+    "Let's analyze word co-occurrence patterns in the press releases to understand which environmental concepts are frequently mentioned together.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 789,
+       "width": 1190
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def get_top_n_bigram(corpus, n=None):\n",
+    "    vec = CountVectorizer(ngram_range=(2, 2)).fit(corpus)\n",
+    "    bag_of_words = vec.transform(corpus)\n",
+    "    sum_words = bag_of_words.sum(axis=0)\n",
+    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
+    "    words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True)\n",
+    "    return words_freq[:n]\n",
+    "\n",
+    "\n",
+    "top_bigrams = get_top_n_bigram(merged_df[\"content\"], n=20)\n",
+    "\n",
+    "# Visualize top bigrams\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "x, y = zip(*top_bigrams)\n",
+    "plt.bar(x, y)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.xlabel(\"Bigram\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.title(\"Top 20 Bigrams in Sierra Club Press Releases\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Sentiment Analysis Comparison\n",
+    "\n",
+    "Compare the sentiment analysis results from the LLM-based few-shot learning approach with a traditional lexicon-based method.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sentiment Analysis Comparison:\n",
+      "textblob_sentiment_category  Neutral  Positive\n",
+      "few_shot_sentiment                            \n",
+      "Negative                           7        52\n",
+      "Neutral                            0        11\n",
+      "Positive                           1        29\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 468,
+       "width": 630
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from textblob import TextBlob\n",
+    "\n",
+    "\n",
+    "def get_sentiment(text):\n",
+    "    return TextBlob(text).sentiment.polarity\n",
+    "\n",
+    "\n",
+    "merged_df[\"textblob_sentiment\"] = merged_df[\"content\"].apply(get_sentiment)\n",
+    "\n",
+    "\n",
+    "def categorize_sentiment(score):\n",
+    "    if score > 0.05:\n",
+    "        return \"Positive\"\n",
+    "    elif score < -0.05:\n",
+    "        return \"Negative\"\n",
+    "    else:\n",
+    "        return \"Neutral\"\n",
+    "\n",
+    "\n",
+    "merged_df[\"textblob_sentiment_category\"] = merged_df[\"textblob_sentiment\"].apply(\n",
+    "    categorize_sentiment\n",
+    ")\n",
+    "\n",
+    "# Compare the results\n",
+    "comparison = pd.crosstab(\n",
+    "    merged_df[\"few_shot_sentiment\"], merged_df[\"textblob_sentiment_category\"]\n",
+    ")\n",
+    "print(\"Sentiment Analysis Comparison:\")\n",
+    "print(comparison)\n",
+    "\n",
+    "# Visualize the comparison\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "comparison.plot(kind=\"bar\", stacked=True)\n",
+    "plt.title(\"Comparison of LLM-based and TextBlob Sentiment Analysis\")\n",
+    "plt.xlabel(\"LLM-based Sentiment\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "plt.legend(title=\"TextBlob Sentiment\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Key Issue Extraction Evaluation\n",
+    "\n",
+    "Evaluate the performance of the LLM-based key issue extraction by comparing it with a traditional keyword extraction method.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Comparison of LLM-based and TF-IDF Keyword Extraction:\n",
+      "                                          key_issues    tfidf_keywords\n",
+      "0  energy efficiency; electricity usage reduction...  the; energy; and\n",
+      "1         water quality; contamination; strip mining       the; of; to\n",
+      "2  habitat destruction; environmental damage; flo...      and; the; of\n",
+      "3  Energy burdens; Historic discrimination; Disin...      the; of; and\n",
+      "4  oil and gas drilling in the Arctic; thermal co...      the; and; to\n",
+      "5  clear cutting land; destruction of Garcia Past...     the; and; rio\n",
+      "6      methane pollution; public lands; polluted air      the; and; to\n",
+      "7  fracked gas pipelines; tolling orders; eminent...      the; to; and\n",
+      "8  fossil fuel electricity generation; clean ener...  the; energy; and\n",
+      "9  air quality; noise pollution; environmental ju...      the; and; of\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "\n",
+    "\n",
+    "def extract_keywords(text, n=3):\n",
+    "    tfidf = TfidfVectorizer(max_features=100)\n",
+    "    response = tfidf.fit_transform([text])\n",
+    "    feature_array = np.array(tfidf.get_feature_names_out())\n",
+    "    tfidf_sorting = np.argsort(response.toarray()).flatten()[::-1]\n",
+    "    return \"; \".join(feature_array[tfidf_sorting][:n])\n",
+    "\n",
+    "\n",
+    "merged_df[\"tfidf_keywords\"] = merged_df[\"content\"].apply(extract_keywords)\n",
+    "\n",
+    "# Compare a sample of results\n",
+    "sample_comparison = merged_df[[\"key_issues\", \"tfidf_keywords\"]].head(10)\n",
+    "print(\"Comparison of LLM-based and TF-IDF Keyword Extraction:\")\n",
+    "print(sample_comparison)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Analysis and Interpretation\n",
+    "\n",
+    "Based on the results of these traditional NLP techniques, analyze and interpret the findings:\n",
+    "\n",
+    "1. How do the traditional classification methods compare to the LLM-based zero-shot classification? Discuss the strengths and weaknesses of each approach.\n",
+    "2. What insights do the LDA topics provide about the main themes in Sierra Club press releases? How do these compare with the zero-shot classification results?\n",
+    "3. What patterns do you observe in the word co-occurrence analysis? How might these inform our understanding of environmental communication strategies?\n",
+    "4. Compare the sentiment analysis results from the LLM-based and lexicon-based methods. What are the implications of any differences for social science research on environmental advocacy?\n",
+    "5. Evaluate the effectiveness of the LLM-based key issue extraction compared to the TF-IDF method. What are the pros and cons of each approach for analyzing environmental communication?\n",
+    "\n",
+    "## 7. Exercise\n",
+    "\n",
+    "For your exercise, please complete the following tasks:\n",
+    "\n",
+    "1. Implement a traditional NLP approach (e.g., rule-based or machine learning) to extract quotes from the press releases. Compare its performance with the LLM-based quote extraction you implemented in Lab 2.\n",
+    "\n",
+    "2. Use a traditional NLP technique (e.g., pattern matching, named entity recognition) to identify organizations mentioned in the press releases. Compare these results with the key issues extracted by the LLM.\n",
+    "\n",
+    "3. Apply a lexical diversity measure (e.g., type-token ratio or MTLD) to analyze the complexity of language used in the press releases. Investigate if there's any correlation between language complexity and the topics or sentiments of the releases.\n",
+    "\n",
+    "4. Create a visualization that combines insights from both traditional NLP techniques and LLM-based analyses. For example, you could create a scatter plot of lexical diversity vs. sentiment, with points colored by the zero-shot classification topic.\n",
+    "\n",
+    "5. Write a brief (300-350 words) comparative analysis of traditional NLP techniques and LLM-based approaches for analyzing environmental communication. Discuss the strengths, weaknesses, and potential complementarities of these methods in the context of social science research.\n",
+    "\n",
+    "Submit your code, visualizations, and written analysis.\n"
+   ]
+  },
+  {
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+   "metadata": {},
+   "source": []
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