diff --git a/Analysis/CCC_analysis.ipynb b/Analysis/CCC_analysis.ipynb
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@@ -0,0 +1,1679 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "b07b3d8e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/kprasath/anaconda3/lib/python3.10/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
+ " warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n",
+ "[nltk_data] Downloading package vader_lexicon to\n",
+ "[nltk_data] /home/kprasath/nltk_data...\n",
+ "[nltk_data] Package vader_lexicon is already up-to-date!\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import json\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import re\n",
+ "import nltk\n",
+ "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
+ "import emoji\n",
+ "from textblob import TextBlob\n",
+ "from fuzzywuzzy import process\n",
+ "import matplotlib.pyplot as plt\n",
+ "from wordcloud import WordCloud\n",
+ "\n",
+ "\n",
+ "nltk.download('vader_lexicon')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a5829c2a",
+ "metadata": {},
+ "source": [
+ "## Getting the Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "c36304cb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>a40_abduction_and_related_offences</th>\n",
+ " <th>b30_burglary_break_and_enter</th>\n",
+ " <th>a10_homicide_and_related_offences</th>\n",
+ " <th>f30_other_government_regulatory_offences</th>\n",
+ " <th>lga_code11</th>\n",
+ " <th>total_division_c_offences</th>\n",
+ " <th>a30_sexual_offences</th>\n",
+ " <th>a70_stalking_harassment_and_threatening_behaviour</th>\n",
+ " <th>e10_justice_procedures</th>\n",
+ " <th>total_division_f_offences</th>\n",
+ " <th>...</th>\n",
+ " <th>total_division_e_offences</th>\n",
+ " <th>e20_breaches_of_orders</th>\n",
+ " <th>f10_regulatory_driving_offences</th>\n",
+ " <th>a60_blackmail_and_extortion</th>\n",
+ " <th>b40_theft</th>\n",
+ " <th>c90_other_drug_offences</th>\n",
+ " <th>f20_transport_regulation_offences</th>\n",
+ " <th>a20_assault_and_related_offences</th>\n",
+ " <th>b60_bribery</th>\n",
+ " <th>c10_drug_dealing_and_trafficking</th>\n",
+ " </tr>\n",
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+ " <td>9.0</td>\n",
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+ " <td>327</td>\n",
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+ " <td>5.0</td>\n",
+ " <td>222</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>176</td>\n",
+ " <td>0.0</td>\n",
+ " <td>24.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>21.0</td>\n",
+ " <td>983</td>\n",
+ " <td>4.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>20570</td>\n",
+ " <td>414</td>\n",
+ " <td>345</td>\n",
+ " <td>236</td>\n",
+ " <td>376</td>\n",
+ " <td>23</td>\n",
+ " <td>...</td>\n",
+ " <td>1884</td>\n",
+ " <td>1508</td>\n",
+ " <td>0.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>3501</td>\n",
+ " <td>5.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>1007</td>\n",
+ " <td>0.0</td>\n",
+ " <td>68.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>9.0</td>\n",
+ " <td>832</td>\n",
+ " <td>4.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>20660</td>\n",
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+ " <td>611</td>\n",
+ " <td>0.0</td>\n",
+ " <td>47.0</td>\n",
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+ " <td>0.0</td>\n",
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+ " <td>640</td>\n",
+ " <td>1.0</td>\n",
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+ " <td>277</td>\n",
+ " <td>0.0</td>\n",
+ " <td>29.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
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+ "<p>5 rows × 37 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " a40_abduction_and_related_offences b30_burglary_break_and_enter \\\n",
+ "0 1.0 27 \n",
+ "1 0.0 69 \n",
+ "2 21.0 983 \n",
+ "3 9.0 832 \n",
+ "4 0.0 205 \n",
+ "\n",
+ " a10_homicide_and_related_offences \\\n",
+ "0 0.0 \n",
+ "1 0.0 \n",
+ "2 4.0 \n",
+ "3 4.0 \n",
+ "4 2.0 \n",
+ "\n",
+ " f30_other_government_regulatory_offences lga_code11 \\\n",
+ "0 1.0 20110 \n",
+ "1 1.0 20260 \n",
+ "2 1.0 20570 \n",
+ "3 1.0 20660 \n",
+ "4 2.0 20740 \n",
+ "\n",
+ " total_division_c_offences a30_sexual_offences \\\n",
+ "0 53 95 \n",
+ "1 137 58 \n",
+ "2 414 345 \n",
+ "3 513 185 \n",
+ "4 154 74 \n",
+ "\n",
+ " a70_stalking_harassment_and_threatening_behaviour \\\n",
+ "0 11 \n",
+ "1 38 \n",
+ "2 236 \n",
+ "3 266 \n",
+ "4 119 \n",
+ "\n",
+ " e10_justice_procedures total_division_f_offences ... \\\n",
+ "0 11 1 ... \n",
+ "1 84 2 ... \n",
+ "2 376 23 ... \n",
+ "3 192 13 ... \n",
+ "4 48 5 ... \n",
+ "\n",
+ " total_division_e_offences e20_breaches_of_orders \\\n",
+ "0 68 57 \n",
+ "1 411 327 \n",
+ "2 1884 1508 \n",
+ "3 2038 1846 \n",
+ "4 378 330 \n",
+ "\n",
+ " f10_regulatory_driving_offences a60_blackmail_and_extortion b40_theft \\\n",
+ "0 0.0 0.0 90 \n",
+ "1 0.0 5.0 222 \n",
+ "2 0.0 7.0 3501 \n",
+ "3 0.0 4.0 2931 \n",
+ "4 0.0 2.0 640 \n",
+ "\n",
+ " c90_other_drug_offences f20_transport_regulation_offences \\\n",
+ "0 0.0 0.0 \n",
+ "1 0.0 0.0 \n",
+ "2 5.0 4.0 \n",
+ "3 1.0 3.0 \n",
+ "4 1.0 2.0 \n",
+ "\n",
+ " a20_assault_and_related_offences b60_bribery \\\n",
+ "0 81 0.0 \n",
+ "1 176 0.0 \n",
+ "2 1007 0.0 \n",
+ "3 611 0.0 \n",
+ "4 277 0.0 \n",
+ "\n",
+ " c10_drug_dealing_and_trafficking \n",
+ "0 9.0 \n",
+ "1 24.0 \n",
+ "2 68.0 \n",
+ "3 47.0 \n",
+ "4 29.0 \n",
+ "\n",
+ "[5 rows x 37 columns]"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#crime data\n",
+ "crime_data = pd.read_csv('crime_data.csv')\n",
+ "crime_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "63621eda",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'pd' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#state data\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m state_data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstate_data.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3\u001b[0m state_data\u001b[38;5;241m.\u001b[39mhead()\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "#state data\n",
+ "state_data = pd.read_csv('state_data.csv')\n",
+ "state_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "005002d0",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "ae7cac44",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 4.27 s, sys: 1.02 s, total: 5.29 s\n",
+ "Wall time: 5.28 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "\n",
+ "#using a bigger data \n",
+ "with open('smallTwitter.json', 'r') as f:\n",
+ " data = json.load(f)\n",
+ "\n",
+ "filtered_data = []\n",
+ "\n",
+ "for tweet in data:\n",
+ " if 'places' in tweet['includes']:\n",
+ " for place in tweet['includes']['places']:\n",
+ " if place['full_name'].endswith(', Victoria'):\n",
+ " area_name, _, _ = place['full_name'].partition(', ')\n",
+ " if '-' in area_name:\n",
+ " area_name = area_name.split('-')[0].strip()\n",
+ "\n",
+ " filtered_data.append({\n",
+ " '_id': tweet['_id'],\n",
+ " #'author_id': tweet['data']['author_id'],\n",
+ " 'text': tweet['data']['text'],\n",
+ " 'sentiment': tweet['data'].get('sentiment', None), # returns None if 'sentiment' key doesn't exist\n",
+ " 'language': tweet['data']['lang'],\n",
+ " 'area_name': area_name\n",
+ " })\n",
+ "\n",
+ "main_data = pd.DataFrame(filtered_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fa806077",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6f5483bb",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "main_data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "067228db",
+ "metadata": {},
+ "source": [
+ "## Data cleaning and transformation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c046c5de",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "print(crime_data.isna().sum())\n",
+ "print(state_data.isna().sum())\n",
+ "print(main_data.isna().sum())\n",
+ "\n",
+ "#Only sentiment has missing values,\n",
+ "#but we can ignore for now cuz we will be engineering this variable later"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4e615112",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "#Same here\n",
+ "print(crime_data.isnull().sum())\n",
+ "print(state_data.isnull().sum())\n",
+ "print(main_data.isnull().sum())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "0f64e183",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Variable Type Adjustments\n",
+ "\n",
+ "#For consistency \n",
+ "crime_data.columns = crime_data.columns.str.strip()\n",
+ "state_data.columns = state_data.columns.str.strip()\n",
+ "main_data.columns = main_data.columns.str.strip()\n",
+ "\n",
+ "crime_data['lga_code11'] = crime_data['lga_code11'].astype(int)\n",
+ "state_data['lga_code'] = state_data['lga_code'].astype(int)\n",
+ "main_data['_id'] = main_data['_id'].astype(str)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b1117bb",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2e98d301",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3598d554",
+ "metadata": {},
+ "source": [
+ "### Text Cleaning"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ba4a5991",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "main_data[['text']].head(5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "62a7dc99",
+ "metadata": {},
+ "source": [
+ "Cleaning the data:\n",
+ "\n",
+ "- Remove URLs: Since URLs generally don't contribute to sentiment, we can remove them.\n",
+ "- Remove mentions: Mentions (@username) may or may not contribute to sentiment, but they can be problematic for analysis due to their variability.\n",
+ "- Remove special characters: These generally don't contribute to sentiment and can make analysis more difficult.\n",
+ "- Remove numbers: Numbers don't contribute to sentiment and can be safely removed.\n",
+ "- Convert to lowercase: Case doesn't matter for sentiment, and converting to lowercase can simplify subsequent analysis.\n",
+ "- Remove extra spaces: Cleaning up extra spaces will make the text more consistent for analysis.\n",
+ "\n",
+ "Feature extraction:\n",
+ "\n",
+ "- Number of hashtags: Hashtags can be indicators of the topic or intensity of a tweet, so we'll count them.\n",
+ "- Number of words: The length of a tweet can be a useful feature.\n",
+ "- Number of characters: This can also be a useful feature.\n",
+ "- Sentiment analysis: We will use NLTK's VADER sentiment analysis tool to compute sentiment scores for each cleaned tweet."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c2098a0",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "main_data.language.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "aa9b575d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 7.76 s, sys: 3.76 ms, total: 7.76 s\n",
+ "Wall time: 7.76 s\n"
+ ]
+ },
+ {
+ "data": {
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+ " <th></th>\n",
+ " <th>_id</th>\n",
+ " <th>text</th>\n",
+ " <th>sentiment</th>\n",
+ " <th>language</th>\n",
+ " <th>area_name</th>\n",
+ " <th>cleaned_text</th>\n",
+ " <th>num_hashtags</th>\n",
+ " <th>num_words</th>\n",
+ " <th>num_chars</th>\n",
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+ " <th>sentiment_score</th>\n",
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+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1414031972041588744</td>\n",
+ " <td>Stella is spending the day with her cousin Mon...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>en</td>\n",
+ " <td>Ocean Grove</td>\n",
+ " <td>stella is spending the day with her cousin monty</td>\n",
+ " <td>0</td>\n",
+ " <td>9</td>\n",
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+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1414032158197354497</td>\n",
+ " <td>@eleaud Oh no! I hope they don’t go up any fur...</td>\n",
+ " <td>0.090909</td>\n",
+ " <td>en</td>\n",
+ " <td>Ocean Grove</td>\n",
+ " <td>oh no i hope they dont go up any further</td>\n",
+ " <td>0</td>\n",
+ " <td>10</td>\n",
+ " <td>40</td>\n",
+ " <td>0</td>\n",
+ " <td>0.5</td>\n",
+ " <td>0.1779</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1414036352308445186</td>\n",
+ " <td>Sunday @afl R17: #aflgiantssuns #afldogsswans ...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>en</td>\n",
+ " <td>Drysdale</td>\n",
+ " <td>sunday r aflgiantssuns afldogsswans afltigerspies</td>\n",
+ " <td>0</td>\n",
+ " <td>5</td>\n",
+ " <td>49</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1414044890040733699</td>\n",
+ " <td>@BlancheVerlie Sorry to watch the declining si...</td>\n",
+ " <td>0.058824</td>\n",
+ " <td>en</td>\n",
+ " <td>Ocean Grove</td>\n",
+ " <td>sorry to watch the declining situation up ther...</td>\n",
+ " <td>0</td>\n",
+ " <td>33</td>\n",
+ " <td>166</td>\n",
+ " <td>0</td>\n",
+ " <td>0.5</td>\n",
+ " <td>0.7717</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>1414057294950649861</td>\n",
+ " <td>@VicGovAu One lady had over 40 saved photos of...</td>\n",
+ " <td>0.090909</td>\n",
+ " <td>en</td>\n",
+ " <td>Drysdale</td>\n",
+ " <td>one lady had over saved photos of login cube s...</td>\n",
+ " <td>0</td>\n",
+ " <td>20</td>\n",
+ " <td>99</td>\n",
+ " <td>0</td>\n",
+ " <td>0.5</td>\n",
+ " <td>0.4215</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " _id text \\\n",
+ "0 1414031972041588744 Stella is spending the day with her cousin Mon... \n",
+ "1 1414032158197354497 @eleaud Oh no! I hope they don’t go up any fur... \n",
+ "2 1414036352308445186 Sunday @afl R17: #aflgiantssuns #afldogsswans ... \n",
+ "3 1414044890040733699 @BlancheVerlie Sorry to watch the declining si... \n",
+ "4 1414057294950649861 @VicGovAu One lady had over 40 saved photos of... \n",
+ "\n",
+ " sentiment language area_name \\\n",
+ "0 0.000000 en Ocean Grove \n",
+ "1 0.090909 en Ocean Grove \n",
+ "2 0.000000 en Drysdale \n",
+ "3 0.058824 en Ocean Grove \n",
+ "4 0.090909 en Drysdale \n",
+ "\n",
+ " cleaned_text num_hashtags num_words \\\n",
+ "0 stella is spending the day with her cousin monty 0 9 \n",
+ "1 oh no i hope they dont go up any further 0 10 \n",
+ "2 sunday r aflgiantssuns afldogsswans afltigerspies 0 5 \n",
+ "3 sorry to watch the declining situation up ther... 0 33 \n",
+ "4 one lady had over saved photos of login cube s... 0 20 \n",
+ "\n",
+ " num_chars num_emojis subjectivity sentiment_score \n",
+ "0 48 0 0.0 0.0000 \n",
+ "1 40 0 0.5 0.1779 \n",
+ "2 49 0 0.0 0.0000 \n",
+ "3 166 0 0.5 0.7717 \n",
+ "4 99 0 0.5 0.4215 "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%time \n",
+ "\n",
+ "# Initialize the sentiment analyzer\n",
+ "sia = SentimentIntensityAnalyzer()\n",
+ "\n",
+ "def clean_text(text):\n",
+ " text = re.sub(r'http\\S+|www.\\S+', '', text) \n",
+ " text = re.sub(r'@\\w+', '', text) \n",
+ " text = re.sub(r'[^A-Za-z\\s]', '', text) \n",
+ " text = re.sub(r'\\d+', '', text) \n",
+ " text = re.sub(r'\\s{2,}', ' ', text) \n",
+ " text = text.lower() \n",
+ " return text.strip() \n",
+ "\n",
+ "def extract_features(text):\n",
+ " num_hashtags = text.count('#')\n",
+ " num_words = len(text.split())\n",
+ " num_chars = len(text)\n",
+ " num_emojis = emoji.emoji_count(text)\n",
+ " return num_hashtags, num_words, num_chars, num_emojis\n",
+ "\n",
+ "def extract_subjectivity(text):\n",
+ " return TextBlob(text).sentiment.subjectivity\n",
+ "\n",
+ "def get_sentiment_score(text):\n",
+ " return sia.polarity_scores(text)['compound']\n",
+ "\n",
+ "# Filter out the non-English tweets from the main data\n",
+ "main_data = main_data[main_data['language'] == 'en'].copy()\n",
+ "\n",
+ "# Apply the cleaning function to the 'text' column\n",
+ "main_data.loc[:, 'cleaned_text'] = main_data['text'].apply(clean_text)\n",
+ "\n",
+ "# Apply the feature extraction function to the cleaned text and create new columns\n",
+ "main_data['num_hashtags'], main_data['num_words'], main_data['num_chars'], main_data['num_emojis'] = zip(*main_data['cleaned_text'].map(extract_features))\n",
+ "\n",
+ "# Apply sentiment analyzer to get the polarity scores and create a new column for that\n",
+ "main_data['subjectivity'] = main_data['cleaned_text'].apply(extract_subjectivity)\n",
+ "\n",
+ "# Add sentiment score\n",
+ "main_data['sentiment_score'] = main_data['cleaned_text'].apply(get_sentiment_score)\n",
+ "\n",
+ "\n",
+ "main_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a4d5c90b",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
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+ " .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>area_name</th>\n",
+ " <th>cleaned_text</th>\n",
+ " <th>num_hashtags</th>\n",
+ " <th>num_words</th>\n",
+ " <th>num_chars</th>\n",
+ " <th>num_emojis</th>\n",
+ " <th>subjectivity</th>\n",
+ " <th>sentiment_score</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Ocean Grove</td>\n",
+ " <td>stella is spending the day with her cousin monty</td>\n",
+ " <td>0</td>\n",
+ " <td>9</td>\n",
+ " <td>48</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Ocean Grove</td>\n",
+ " <td>oh no i hope they dont go up any further</td>\n",
+ " <td>0</td>\n",
+ " <td>10</td>\n",
+ " <td>40</td>\n",
+ " <td>0</td>\n",
+ " <td>0.5</td>\n",
+ " <td>0.1779</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Drysdale</td>\n",
+ " <td>sunday r aflgiantssuns afldogsswans afltigerspies</td>\n",
+ " <td>0</td>\n",
+ " <td>5</td>\n",
+ " <td>49</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>Ocean Grove</td>\n",
+ " <td>sorry to watch the declining situation up ther...</td>\n",
+ " <td>0</td>\n",
+ " <td>33</td>\n",
+ " <td>166</td>\n",
+ " <td>0</td>\n",
+ " <td>0.5</td>\n",
+ " <td>0.7717</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>Drysdale</td>\n",
+ " <td>one lady had over saved photos of login cube s...</td>\n",
+ " <td>0</td>\n",
+ " <td>20</td>\n",
+ " <td>99</td>\n",
+ " <td>0</td>\n",
+ " <td>0.5</td>\n",
+ " <td>0.4215</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " area_name cleaned_text \\\n",
+ "0 Ocean Grove stella is spending the day with her cousin monty \n",
+ "1 Ocean Grove oh no i hope they dont go up any further \n",
+ "2 Drysdale sunday r aflgiantssuns afldogsswans afltigerspies \n",
+ "3 Ocean Grove sorry to watch the declining situation up ther... \n",
+ "4 Drysdale one lady had over saved photos of login cube s... \n",
+ "\n",
+ " num_hashtags num_words num_chars num_emojis subjectivity \\\n",
+ "0 0 9 48 0 0.0 \n",
+ "1 0 10 40 0 0.5 \n",
+ "2 0 5 49 0 0.0 \n",
+ "3 0 33 166 0 0.5 \n",
+ "4 0 20 99 0 0.5 \n",
+ "\n",
+ " sentiment_score \n",
+ "0 0.0000 \n",
+ "1 0.1779 \n",
+ "2 0.0000 \n",
+ "3 0.7717 \n",
+ "4 0.4215 "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "text_data = main_data.drop(['_id', 'text', 'sentiment', 'language'], axis=1)\n",
+ "text_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "af84dddc",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'state_data' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mstate_data\u001b[49m\u001b[38;5;241m.\u001b[39mdtypes\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'state_data' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "state_data.dtypes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "0ef3468a",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'state_data' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mstate_data\u001b[49m\u001b[38;5;241m.\u001b[39mhead()\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'state_data' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "state_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "d970d153",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " .dataframe tbody tr th {\n",
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+ " }\n",
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+ " .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>a40_abduction_and_related_offences</th>\n",
+ " <th>b30_burglary_break_and_enter</th>\n",
+ " <th>a10_homicide_and_related_offences</th>\n",
+ " <th>f30_other_government_regulatory_offences</th>\n",
+ " <th>lga_code11</th>\n",
+ " <th>total_division_c_offences</th>\n",
+ " <th>a30_sexual_offences</th>\n",
+ " <th>a70_stalking_harassment_and_threatening_behaviour</th>\n",
+ " <th>e10_justice_procedures</th>\n",
+ " <th>total_division_f_offences</th>\n",
+ " <th>...</th>\n",
+ " <th>total_division_e_offences</th>\n",
+ " <th>e20_breaches_of_orders</th>\n",
+ " <th>f10_regulatory_driving_offences</th>\n",
+ " <th>a60_blackmail_and_extortion</th>\n",
+ " <th>b40_theft</th>\n",
+ " <th>c90_other_drug_offences</th>\n",
+ " <th>f20_transport_regulation_offences</th>\n",
+ " <th>a20_assault_and_related_offences</th>\n",
+ " <th>b60_bribery</th>\n",
+ " <th>c10_drug_dealing_and_trafficking</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1.0</td>\n",
+ " <td>27</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>20110</td>\n",
+ " <td>53</td>\n",
+ " <td>95</td>\n",
+ " <td>11</td>\n",
+ " <td>11</td>\n",
+ " <td>1</td>\n",
+ " <td>...</td>\n",
+ " <td>68</td>\n",
+ " <td>57</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>90</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>81</td>\n",
+ " <td>0.0</td>\n",
+ " <td>9.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0.0</td>\n",
+ " <td>69</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>20260</td>\n",
+ " <td>137</td>\n",
+ " <td>58</td>\n",
+ " <td>38</td>\n",
+ " <td>84</td>\n",
+ " <td>2</td>\n",
+ " <td>...</td>\n",
+ " <td>411</td>\n",
+ " <td>327</td>\n",
+ " <td>0.0</td>\n",
+ " <td>5.0</td>\n",
+ " <td>222</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>176</td>\n",
+ " <td>0.0</td>\n",
+ " <td>24.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>21.0</td>\n",
+ " <td>983</td>\n",
+ " <td>4.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>20570</td>\n",
+ " <td>414</td>\n",
+ " <td>345</td>\n",
+ " <td>236</td>\n",
+ " <td>376</td>\n",
+ " <td>23</td>\n",
+ " <td>...</td>\n",
+ " <td>1884</td>\n",
+ " <td>1508</td>\n",
+ " <td>0.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>3501</td>\n",
+ " <td>5.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>1007</td>\n",
+ " <td>0.0</td>\n",
+ " <td>68.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>9.0</td>\n",
+ " <td>832</td>\n",
+ " <td>4.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>20660</td>\n",
+ " <td>513</td>\n",
+ " <td>185</td>\n",
+ " <td>266</td>\n",
+ " <td>192</td>\n",
+ " <td>13</td>\n",
+ " <td>...</td>\n",
+ " <td>2038</td>\n",
+ " <td>1846</td>\n",
+ " <td>0.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>2931</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>611</td>\n",
+ " <td>0.0</td>\n",
+ " <td>47.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0.0</td>\n",
+ " <td>205</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>20740</td>\n",
+ " <td>154</td>\n",
+ " <td>74</td>\n",
+ " <td>119</td>\n",
+ " <td>48</td>\n",
+ " <td>5</td>\n",
+ " <td>...</td>\n",
+ " <td>378</td>\n",
+ " <td>330</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>640</td>\n",
+ " <td>1.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>277</td>\n",
+ " <td>0.0</td>\n",
+ " <td>29.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 37 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " a40_abduction_and_related_offences b30_burglary_break_and_enter \\\n",
+ "0 1.0 27 \n",
+ "1 0.0 69 \n",
+ "2 21.0 983 \n",
+ "3 9.0 832 \n",
+ "4 0.0 205 \n",
+ "\n",
+ " a10_homicide_and_related_offences \\\n",
+ "0 0.0 \n",
+ "1 0.0 \n",
+ "2 4.0 \n",
+ "3 4.0 \n",
+ "4 2.0 \n",
+ "\n",
+ " f30_other_government_regulatory_offences lga_code11 \\\n",
+ "0 1.0 20110 \n",
+ "1 1.0 20260 \n",
+ "2 1.0 20570 \n",
+ "3 1.0 20660 \n",
+ "4 2.0 20740 \n",
+ "\n",
+ " total_division_c_offences a30_sexual_offences \\\n",
+ "0 53 95 \n",
+ "1 137 58 \n",
+ "2 414 345 \n",
+ "3 513 185 \n",
+ "4 154 74 \n",
+ "\n",
+ " a70_stalking_harassment_and_threatening_behaviour e10_justice_procedures \\\n",
+ "0 11 11 \n",
+ "1 38 84 \n",
+ "2 236 376 \n",
+ "3 266 192 \n",
+ "4 119 48 \n",
+ "\n",
+ " total_division_f_offences ... total_division_e_offences \\\n",
+ "0 1 ... 68 \n",
+ "1 2 ... 411 \n",
+ "2 23 ... 1884 \n",
+ "3 13 ... 2038 \n",
+ "4 5 ... 378 \n",
+ "\n",
+ " e20_breaches_of_orders f10_regulatory_driving_offences \\\n",
+ "0 57 0.0 \n",
+ "1 327 0.0 \n",
+ "2 1508 0.0 \n",
+ "3 1846 0.0 \n",
+ "4 330 0.0 \n",
+ "\n",
+ " a60_blackmail_and_extortion b40_theft c90_other_drug_offences \\\n",
+ "0 0.0 90 0.0 \n",
+ "1 5.0 222 0.0 \n",
+ "2 7.0 3501 5.0 \n",
+ "3 4.0 2931 1.0 \n",
+ "4 2.0 640 1.0 \n",
+ "\n",
+ " f20_transport_regulation_offences a20_assault_and_related_offences \\\n",
+ "0 0.0 81 \n",
+ "1 0.0 176 \n",
+ "2 4.0 1007 \n",
+ "3 3.0 611 \n",
+ "4 2.0 277 \n",
+ "\n",
+ " b60_bribery c10_drug_dealing_and_trafficking \n",
+ "0 0.0 9.0 \n",
+ "1 0.0 24.0 \n",
+ "2 0.0 68.0 \n",
+ "3 0.0 47.0 \n",
+ "4 0.0 29.0 \n",
+ "\n",
+ "[5 rows x 37 columns]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "crime_data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bb62f0d1",
+ "metadata": {},
+ "source": [
+ "## Data integration & Mapping"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0960e5a5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "state_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "076dd0c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lga_code_mapping = state_data.set_index('scc_name')['lga_code']\n",
+ "\n",
+ "text_data['lga_code'] = text_data['area_name'].map(lga_code_mapping)\n",
+ "\n",
+ "state_data_unique = state_data.groupby('lga_code').agg({\n",
+ " 'lga_name': 'first',\n",
+ " 'lat': 'mean',\n",
+ " 'lon': 'mean'\n",
+ "}).reset_index()\n",
+ "\n",
+ "text_data = text_data.merge(state_data_unique, on='lga_code', how='left')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "eb8d7900",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "<style scoped>\n",
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+ " }\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>area_name</th>\n",
+ " <th>cleaned_text</th>\n",
+ " <th>num_hashtags</th>\n",
+ " <th>num_words</th>\n",
+ " <th>num_chars</th>\n",
+ " <th>num_emojis</th>\n",
+ " <th>subjectivity</th>\n",
+ " <th>sentiment_score</th>\n",
+ " <th>lga_code</th>\n",
+ " <th>lga_name</th>\n",
+ " <th>lat</th>\n",
+ " <th>lon</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Ocean Grove</td>\n",
+ " <td>stella is spending the day with her cousin monty</td>\n",
+ " <td>0</td>\n",
+ " <td>9</td>\n",
+ " <td>48</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0000</td>\n",
+ " <td>22750.0</td>\n",
+ " <td>Greater Geelong</td>\n",
+ " <td>-38.145829</td>\n",
+ " <td>144.416254</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Ocean Grove</td>\n",
+ " <td>oh no i hope they dont go up any further</td>\n",
+ " <td>0</td>\n",
+ " <td>10</td>\n",
+ " <td>40</td>\n",
+ " <td>0</td>\n",
+ " <td>0.5</td>\n",
+ " <td>0.1779</td>\n",
+ " <td>22750.0</td>\n",
+ " <td>Greater Geelong</td>\n",
+ " <td>-38.145829</td>\n",
+ " <td>144.416254</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Drysdale</td>\n",
+ " <td>sunday r aflgiantssuns afldogsswans afltigerspies</td>\n",
+ " <td>0</td>\n",
+ " <td>5</td>\n",
+ " <td>49</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0000</td>\n",
+ " <td>22750.0</td>\n",
+ " <td>Greater Geelong</td>\n",
+ " <td>-38.145829</td>\n",
+ " <td>144.416254</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>Ocean Grove</td>\n",
+ " <td>sorry to watch the declining situation up ther...</td>\n",
+ " <td>0</td>\n",
+ " <td>33</td>\n",
+ " <td>166</td>\n",
+ " <td>0</td>\n",
+ " <td>0.5</td>\n",
+ " <td>0.7717</td>\n",
+ " <td>22750.0</td>\n",
+ " <td>Greater Geelong</td>\n",
+ " <td>-38.145829</td>\n",
+ " <td>144.416254</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>Drysdale</td>\n",
+ " <td>one lady had over saved photos of login cube s...</td>\n",
+ " <td>0</td>\n",
+ " <td>20</td>\n",
+ " <td>99</td>\n",
+ " <td>0</td>\n",
+ " <td>0.5</td>\n",
+ " <td>0.4215</td>\n",
+ " <td>22750.0</td>\n",
+ " <td>Greater Geelong</td>\n",
+ " <td>-38.145829</td>\n",
+ " <td>144.416254</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " area_name cleaned_text \\\n",
+ "0 Ocean Grove stella is spending the day with her cousin monty \n",
+ "1 Ocean Grove oh no i hope they dont go up any further \n",
+ "2 Drysdale sunday r aflgiantssuns afldogsswans afltigerspies \n",
+ "3 Ocean Grove sorry to watch the declining situation up ther... \n",
+ "4 Drysdale one lady had over saved photos of login cube s... \n",
+ "\n",
+ " num_hashtags num_words num_chars num_emojis subjectivity \\\n",
+ "0 0 9 48 0 0.0 \n",
+ "1 0 10 40 0 0.5 \n",
+ "2 0 5 49 0 0.0 \n",
+ "3 0 33 166 0 0.5 \n",
+ "4 0 20 99 0 0.5 \n",
+ "\n",
+ " sentiment_score lga_code lga_name lat lon \n",
+ "0 0.0000 22750.0 Greater Geelong -38.145829 144.416254 \n",
+ "1 0.1779 22750.0 Greater Geelong -38.145829 144.416254 \n",
+ "2 0.0000 22750.0 Greater Geelong -38.145829 144.416254 \n",
+ "3 0.7717 22750.0 Greater Geelong -38.145829 144.416254 \n",
+ "4 0.4215 22750.0 Greater Geelong -38.145829 144.416254 "
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "text_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "91fcfdbb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#crim data merging\n",
+ "\n",
+ "# Assuming crime_data and state_data are your two dataframes\n",
+ "# and 'lga_code' is the name of the column with the LGA code in both dataframes\n",
+ "# and 'lga_code11' is the column name in crime_data\n",
+ "\n",
+ "# First, make sure the 'lga_code' columns in both dataframes are of the same type\n",
+ "crime_data['lga_code11'] = crime_data['lga_code11'].astype(str)\n",
+ "state_data['lga_code'] = state_data['lga_code'].astype(str)\n",
+ "\n",
+ "# Then, merge the dataframes\n",
+ "merged_data = pd.merge(crime_data, state_data, left_on='lga_code11', right_on='lga_code', how='left')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "a31d6efe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#merged_data.to_csv('crime_spatial.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "3e96a60f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#text_data.to_csv('textdatafor_spatial.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e3c87512",
+ "metadata": {},
+ "source": [
+ "# Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "68f5899b",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "\"['new_sentiment', 'compound'] not in index\"",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtext_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnum_hashtags\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnum_words\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnum_chars\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnew_sentiment\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnum_emojis\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcompound\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msubjectivity\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mdescribe()\n",
+ "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/core/frame.py:3813\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3811\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m 3812\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m-> 3813\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 3815\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m 3816\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n",
+ "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/core/indexes/base.py:6070\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m 6067\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 6068\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 6070\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6072\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m 6073\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[1;32m 6074\u001b[0m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
+ "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/core/indexes/base.py:6133\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m 6130\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 6132\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m-> 6133\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "\u001b[0;31mKeyError\u001b[0m: \"['new_sentiment', 'compound'] not in index\""
+ ]
+ }
+ ],
+ "source": [
+ "#text_data[['num_hashtags', 'num_words', 'num_chars', 'new_sentiment', 'num_emojis', 'compound', 'subjectivity']].describe()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5db957df",
+ "metadata": {},
+ "source": [
+ "plt.hist(text_data['new_sentiment'], bins=20, alpha=0.5)\n",
+ "plt.xlabel('Sentiment Score')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Sentiment Distribution')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "4b8425bb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1200x600 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
+ "\n",
+ "# Plot 1: Sentiment Distribution\n",
+ "axes[0].hist(text_data['sentiment_score'], bins=20, alpha=0.5)\n",
+ "axes[0].set_xlabel('Sentiment Score')\n",
+ "axes[0].set_ylabel('Frequency')\n",
+ "axes[0].set_title('Sentiment Distribution')\n",
+ "\n",
+ "# Plot 2: Subjectivity Distribution\n",
+ "axes[1].hist(text_data['subjectivity'], bins=20, alpha=0.5)\n",
+ "axes[1].set_xlabel('Subjectivity Score')\n",
+ "axes[1].set_ylabel('Frequency')\n",
+ "axes[1].set_title('Subjectivity Distribution')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6385dcdb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# takes way too long, wont be scalable in big data\n",
+ "'''from wordcloud import WordCloud\n",
+ "\n",
+ "wordcloud = WordCloud(width = 800, height = 800, \n",
+ " background_color ='white', \n",
+ " stopwords = None, \n",
+ " min_font_size = 10).generate(' '.join(text_data['cleaned_text']))\n",
+ "\n",
+ "plt.figure(figsize = (8, 8), facecolor = None) \n",
+ "plt.imshow(wordcloud) \n",
+ "plt.axis(\"off\") \n",
+ "plt.tight_layout(pad = 0) \n",
+ "plt.show()'''\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a2b43468",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "#taking a very long time to run\n",
+ "\n",
+ "import geopandas as gpd\n",
+ "\n",
+ "gdf = gpd.GeoDataFrame(text_data, geometry=gpd.points_from_xy(text_data.lon_x, text_data.lat_x))\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "gdf.plot(ax=ax, color='blue')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "697b8a18",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "text_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "80f4bd71",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip3 install geoplot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6814b63c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "import geopandas as gpd\n",
+ "import geoplot as gplt\n",
+ "import geoplot.crs as gcrs\n",
+ "import pandas as pd\n",
+ "from shapely import wkt\n",
+ "\n",
+ "def load_wkt(value):\n",
+ " if isinstance(value, str):\n",
+ " return wkt.loads(value)\n",
+ " return None\n",
+ "\n",
+ "text_data['geometry_y'] = text_data['geometry_y'].apply(load_wkt) # convert POLYGON string to geometry\n",
+ "gdf = gpd.GeoDataFrame(text_data, geometry='geometry_y') # convert DataFrame to GeoDataFrame\n",
+ "\n",
+ "# Group by 'scc_name_x' and calculate mean sentiment\n",
+ "gdf['new_sentiment'] = pd.to_numeric(gdf['new_sentiment'], errors='coerce')\n",
+ "grouped_gdf = gdf.groupby('scc_name_x').agg({'new_sentiment': 'mean'})\n",
+ "\n",
+ "# Join the original gdf with grouped_gdf to get the mean sentiment for each row\n",
+ "gdf = gdf.merge(grouped_gdf, on='scc_name_x')\n",
+ "\n",
+ "# Create a KDE plot and overlay it on a polygon plot\n",
+ "ax = gplt.kdeplot(\n",
+ " gdf,\n",
+ " clip=gdf.geometry,\n",
+ " shade=True,\n",
+ " cmap='Reds',\n",
+ " projection=gcrs.AlbersEqualArea(),\n",
+ ")\n",
+ "gplt.polyplot(gdf, ax=ax, zorder=1)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ac8ec1f6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "text_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "586cf916",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "\n",
+ "import geopandas as gpd\n",
+ "import geoplot as gplt\n",
+ "import geoplot.crs as gcrs\n",
+ "from shapely.geometry import Point\n",
+ "\n",
+ "# Convert lat/lon to a shapely Point object\n",
+ "text_data['geometry'] = text_data.apply(lambda row: Point(row.lon, row.lat), axis=1)\n",
+ "\n",
+ "# Convert DataFrame to GeoDataFrame\n",
+ "gdf = gpd.GeoDataFrame(text_data, geometry='geometry')\n",
+ "\n",
+ "# Group by 'lga_name' and calculate mean sentiment_score\n",
+ "gdf['sentiment_score'] = pd.to_numeric(gdf['sentiment_score'], errors='coerce')\n",
+ "grouped_gdf = gdf.groupby('lga_name').agg({'sentiment_score': 'mean'})\n",
+ "\n",
+ "# Join the original gdf with grouped_gdf to get the mean sentiment for each row\n",
+ "gdf = gdf.merge(grouped_gdf, on='lga_name', suffixes=('', '_mean'))\n",
+ "\n",
+ "# Create a KDE plot and overlay it on a polygon plot\n",
+ "ax = gplt.kdeplot(\n",
+ " gdf,\n",
+ " hue='sentiment_score_mean',\n",
+ " clip=gdf.geometry,\n",
+ " shade=True,\n",
+ " cmap='coolwarm',\n",
+ " projection=gcrs.AlbersEqualArea(),\n",
+ ")\n",
+ "gplt.polyplot(gdf, ax=ax, zorder=1)\n"
+ ]
+ }
+ ],
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