cyberbullying-detection-nlp.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Dataset Cyberbullying Detection using NLP\n","---"]},{"cell_type":"markdown","metadata":{},"source":["### Imports\n"]},{"cell_type":"code","execution_count":36,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:56.337118Z","iopub.status.busy":"2023-03-04T08:30:56.336049Z","iopub.status.idle":"2023-03-04T08:30:56.343741Z","shell.execute_reply":"2023-03-04T08:30:56.342607Z","shell.execute_reply.started":"2023-03-04T08:30:56.337074Z"},"trusted":true},"outputs":[],"source":["import pandas as pd\n","import re\n","import nltk\n","from nltk.corpus import stopwords\n","from nltk.stem import PorterStemmer\n","from sklearn.feature_extraction.text import CountVectorizer\n","\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","from tqdm.auto import tqdm\n","import time"]},{"cell_type":"markdown","metadata":{},"source":["### Loading the Dataset"]},{"cell_type":"code","execution_count":37,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:56.350479Z","iopub.status.busy":"2023-03-04T08:30:56.349639Z","iopub.status.idle":"2023-03-04T08:30:56.442155Z","shell.execute_reply":"2023-03-04T08:30:56.440917Z","shell.execute_reply.started":"2023-03-04T08:30:56.350428Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>tweet_text</th>\n","      <th>cyberbullying_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>In other words #katandandre, your food was cra...</td>\n","      <td>not_cyberbullying</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Why is #aussietv so white? #MKR #theblock #ImA...</td>\n","      <td>not_cyberbullying</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>@XochitlSuckkks a classy whore? Or more red ve...</td>\n","      <td>not_cyberbullying</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>@Jason_Gio meh. :P  thanks for the heads up, b...</td>\n","      <td>not_cyberbullying</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>@RudhoeEnglish This is an ISIS account pretend...</td>\n","      <td>not_cyberbullying</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>19995</th>\n","      <td>I am Christian who definitely does not support...</td>\n","      <td>religion</td>\n","    </tr>\n","    <tr>\n","      <th>19996</th>\n","      <td>@yousufpoosuf @RKingLive2Dance @izrinhariri No...</td>\n","      <td>religion</td>\n","    </tr>\n","    <tr>\n","      <th>19997</th>\n","      <td>How can you say Barack Obama was the best pres...</td>\n","      <td>religion</td>\n","    </tr>\n","    <tr>\n","      <th>19998</th>\n","      <td>Another Mentally ill asylum seeker. WTHell do ...</td>\n","      <td>religion</td>\n","    </tr>\n","    <tr>\n","      <th>19999</th>\n","      <td>shame on you. Bhagat singh did not kill innoce...</td>\n","      <td>religion</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>20000 rows × 2 columns</p>\n","</div>"],"text/plain":["                                              tweet_text cyberbullying_type\n","0      In other words #katandandre, your food was cra...  not_cyberbullying\n","1      Why is #aussietv so white? #MKR #theblock #ImA...  not_cyberbullying\n","2      @XochitlSuckkks a classy whore? Or more red ve...  not_cyberbullying\n","3      @Jason_Gio meh. :P  thanks for the heads up, b...  not_cyberbullying\n","4      @RudhoeEnglish This is an ISIS account pretend...  not_cyberbullying\n","...                                                  ...                ...\n","19995  I am Christian who definitely does not support...           religion\n","19996  @yousufpoosuf @RKingLive2Dance @izrinhariri No...           religion\n","19997  How can you say Barack Obama was the best pres...           religion\n","19998  Another Mentally ill asylum seeker. WTHell do ...           religion\n","19999  shame on you. Bhagat singh did not kill innoce...           religion\n","\n","[20000 rows x 2 columns]"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv('/kaggle/input/cyberbullying-classification/cyberbullying_tweets.csv', nrows = 20000)\n","df"]},{"cell_type":"code","execution_count":38,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:56.445548Z","iopub.status.busy":"2023-03-04T08:30:56.444521Z","iopub.status.idle":"2023-03-04T08:30:56.455197Z","shell.execute_reply":"2023-03-04T08:30:56.454169Z","shell.execute_reply.started":"2023-03-04T08:30:56.445507Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>tweet_text</th>\n","      <th>cyberbullying_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>In other words #katandandre, your food was cra...</td>\n","      <td>not_cyberbullying</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Why is #aussietv so white? #MKR #theblock #ImA...</td>\n","      <td>not_cyberbullying</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>@XochitlSuckkks a classy whore? Or more red ve...</td>\n","      <td>not_cyberbullying</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>@Jason_Gio meh. :P  thanks for the heads up, b...</td>\n","      <td>not_cyberbullying</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>@RudhoeEnglish This is an ISIS account pretend...</td>\n","      <td>not_cyberbullying</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                          tweet_text cyberbullying_type\n","0  In other words #katandandre, your food was cra...  not_cyberbullying\n","1  Why is #aussietv so white? #MKR #theblock #ImA...  not_cyberbullying\n","2  @XochitlSuckkks a classy whore? Or more red ve...  not_cyberbullying\n","3  @Jason_Gio meh. :P  thanks for the heads up, b...  not_cyberbullying\n","4  @RudhoeEnglish This is an ISIS account pretend...  not_cyberbullying"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":39,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:56.456621Z","iopub.status.busy":"2023-03-04T08:30:56.456231Z","iopub.status.idle":"2023-03-04T08:30:56.474705Z","shell.execute_reply":"2023-03-04T08:30:56.473737Z","shell.execute_reply.started":"2023-03-04T08:30:56.456587Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>tweet_text</th>\n","      <th>cyberbullying_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>19995</th>\n","      <td>I am Christian who definitely does not support...</td>\n","      <td>religion</td>\n","    </tr>\n","    <tr>\n","      <th>19996</th>\n","      <td>@yousufpoosuf @RKingLive2Dance @izrinhariri No...</td>\n","      <td>religion</td>\n","    </tr>\n","    <tr>\n","      <th>19997</th>\n","      <td>How can you say Barack Obama was the best pres...</td>\n","      <td>religion</td>\n","    </tr>\n","    <tr>\n","      <th>19998</th>\n","      <td>Another Mentally ill asylum seeker. WTHell do ...</td>\n","      <td>religion</td>\n","    </tr>\n","    <tr>\n","      <th>19999</th>\n","      <td>shame on you. Bhagat singh did not kill innoce...</td>\n","      <td>religion</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                              tweet_text cyberbullying_type\n","19995  I am Christian who definitely does not support...           religion\n","19996  @yousufpoosuf @RKingLive2Dance @izrinhariri No...           religion\n","19997  How can you say Barack Obama was the best pres...           religion\n","19998  Another Mentally ill asylum seeker. WTHell do ...           religion\n","19999  shame on you. Bhagat singh did not kill innoce...           religion"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["df.tail()"]},{"cell_type":"code","execution_count":40,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:56.477216Z","iopub.status.busy":"2023-03-04T08:30:56.476461Z","iopub.status.idle":"2023-03-04T08:30:56.486208Z","shell.execute_reply":"2023-03-04T08:30:56.485052Z","shell.execute_reply.started":"2023-03-04T08:30:56.477167Z"},"trusted":true},"outputs":[{"data":{"text/plain":["(20000, 2)"]},"execution_count":40,"metadata":{},"output_type":"execute_result"}],"source":["df.shape"]},{"cell_type":"markdown","metadata":{},"source":["# EDA\n","---"]},{"cell_type":"markdown","metadata":{},"source":["### 1. Handling Null Values"]},{"cell_type":"code","execution_count":41,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:56.953066Z","iopub.status.busy":"2023-03-04T08:30:56.952651Z","iopub.status.idle":"2023-03-04T08:30:56.965768Z","shell.execute_reply":"2023-03-04T08:30:56.964554Z","shell.execute_reply.started":"2023-03-04T08:30:56.953030Z"},"trusted":true},"outputs":[{"data":{"text/plain":["tweet_text            False\n","cyberbullying_type    False\n","dtype: bool"]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":["df.isna().any()"]},{"cell_type":"code","execution_count":42,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:57.026040Z","iopub.status.busy":"2023-03-04T08:30:57.025164Z","iopub.status.idle":"2023-03-04T08:30:57.038563Z","shell.execute_reply":"2023-03-04T08:30:57.037179Z","shell.execute_reply.started":"2023-03-04T08:30:57.025993Z"},"trusted":true},"outputs":[{"data":{"text/plain":["tweet_text            0\n","cyberbullying_type    0\n","dtype: int64"]},"execution_count":42,"metadata":{},"output_type":"execute_result"}],"source":["df.isna().sum()"]},{"cell_type":"markdown","metadata":{},"source":["### 2. Handling Duplicate Values"]},{"cell_type":"code","execution_count":43,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:57.041404Z","iopub.status.busy":"2023-03-04T08:30:57.040943Z","iopub.status.idle":"2023-03-04T08:30:57.064930Z","shell.execute_reply":"2023-03-04T08:30:57.064055Z","shell.execute_reply.started":"2023-03-04T08:30:57.041359Z"},"trusted":true},"outputs":[{"data":{"text/plain":["tweet_text            19915\n","cyberbullying_type        3\n","dtype: int64"]},"execution_count":43,"metadata":{},"output_type":"execute_result"}],"source":["df.nunique()"]},{"cell_type":"code","execution_count":44,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:57.068035Z","iopub.status.busy":"2023-03-04T08:30:57.067146Z","iopub.status.idle":"2023-03-04T08:30:57.086314Z","shell.execute_reply":"2023-03-04T08:30:57.085197Z","shell.execute_reply.started":"2023-03-04T08:30:57.067987Z"},"trusted":true},"outputs":[{"data":{"text/plain":["19915"]},"execution_count":44,"metadata":{},"output_type":"execute_result"}],"source":["df['tweet_text'].nunique()"]},{"cell_type":"markdown","metadata":{},"source":["### 3. Class Distributions"]},{"cell_type":"code","execution_count":45,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:57.089957Z","iopub.status.busy":"2023-03-04T08:30:57.089317Z","iopub.status.idle":"2023-03-04T08:30:57.098933Z","shell.execute_reply":"2023-03-04T08:30:57.097823Z","shell.execute_reply.started":"2023-03-04T08:30:57.089921Z"},"trusted":true},"outputs":[{"data":{"text/plain":["gender               7973\n","not_cyberbullying    7945\n","religion             4082\n","Name: cyberbullying_type, dtype: int64"]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["df['cyberbullying_type'].value_counts()"]},{"cell_type":"code","execution_count":46,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:57.100545Z","iopub.status.busy":"2023-03-04T08:30:57.100223Z","iopub.status.idle":"2023-03-04T08:30:57.324131Z","shell.execute_reply":"2023-03-04T08:30:57.323182Z","shell.execute_reply.started":"2023-03-04T08:30:57.100518Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Create a bar plot of the class distribution\n","class_counts = df['cyberbullying_type'].value_counts()\n","class_counts.plot(kind='bar')\n","plt.title('Class Distribution of Cyberbullying Types')\n","plt.xlabel('Labels')\n","plt.ylabel('Number of Tweets')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### 4. Word Count"]},{"cell_type":"code","execution_count":47,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:57.325726Z","iopub.status.busy":"2023-03-04T08:30:57.325366Z","iopub.status.idle":"2023-03-04T08:30:57.330770Z","shell.execute_reply":"2023-03-04T08:30:57.329390Z","shell.execute_reply.started":"2023-03-04T08:30:57.325692Z"},"trusted":true},"outputs":[],"source":["from collections import Counter\n","import re\n","\n","import nltk\n","from nltk.corpus import stopwords"]},{"cell_type":"code","execution_count":48,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:57.710965Z","iopub.status.busy":"2023-03-04T08:30:57.710574Z","iopub.status.idle":"2023-03-04T08:30:57.812828Z","shell.execute_reply":"2023-03-04T08:30:57.811685Z","shell.execute_reply.started":"2023-03-04T08:30:57.710930Z"},"trusted":true},"outputs":[],"source":["# Concatenate all tweet texts into a single string\n","all_text = ' '.join(df['tweet_text'].values)\n","# Remove URLs, mentions, and hashtags from the text\n","all_text = re.sub(r'http\\S+', '', all_text)\n","all_text = re.sub(r'@\\S+', '', all_text)\n","all_text = re.sub(r'#\\S+', '', all_text)\n","# Split the text into individual words\n","words = all_text.split()"]},{"cell_type":"code","execution_count":49,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:57.815827Z","iopub.status.busy":"2023-03-04T08:30:57.815018Z","iopub.status.idle":"2023-03-04T08:30:57.879273Z","shell.execute_reply":"2023-03-04T08:30:57.878314Z","shell.execute_reply.started":"2023-03-04T08:30:57.815792Z"},"trusted":true},"outputs":[],"source":["# Remove stop words\n","stop_words = set(stopwords.words('english'))\n","words = [word for word in words if not word in stop_words]"]},{"cell_type":"code","execution_count":50,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:57.881162Z","iopub.status.busy":"2023-03-04T08:30:57.880717Z","iopub.status.idle":"2023-03-04T08:30:57.944345Z","shell.execute_reply":"2023-03-04T08:30:57.943354Z","shell.execute_reply.started":"2023-03-04T08:30:57.881091Z"},"trusted":true},"outputs":[{"data":{"text/plain":["[('I', 5256),\n"," ('rape', 2962),\n"," ('gay', 2507),\n"," ('RT', 2103),\n"," ('jokes', 2038),\n"," ('like', 1739),\n"," ('people', 1336),\n"," (\"I'm\", 1167),\n"," ('&amp;', 1117),\n"," ('joke', 1114),\n"," ('The', 1021),\n"," ('call', 955),\n"," ('u', 942),\n"," ('You', 932),\n"," ('think', 896),\n"," ('get', 863),\n"," ('make', 862),\n"," ('women', 811),\n"," ('know', 739),\n"," ('one', 738),\n"," ('Muslim', 733),\n"," ('Muslims', 728),\n"," ('female', 703),\n"," ('sexist', 668),\n"," ('-', 650),\n"," ('And', 638),\n"," ('would', 637),\n"," ('jokes,', 615),\n"," ('Christian', 605),\n"," ('woman', 571),\n"," ('If', 561),\n"," ('support', 560),\n"," ('say', 542),\n"," ('even', 523),\n"," ('want', 520),\n"," ('see', 512),\n"," ('right', 504),\n"," ('men', 502),\n"," ('idiots', 488),\n"," ('never', 483),\n"," ('still', 469),\n"," ('But', 467),\n"," ('made', 458),\n"," ('go', 456),\n"," ('really', 451),\n"," ('idiot', 447),\n"," ('This', 445),\n"," ('A', 444),\n"," ('So', 443),\n"," ('making', 443),\n"," ('.', 430),\n"," ('Islamic', 429),\n"," ('bitch', 428),\n"," ('need', 418),\n"," (\"can't\", 412),\n"," ('hate', 410),\n"," ('Rape', 409),\n"," ('Kat', 399),\n"," ('What', 390),\n"," ('They', 386),\n"," ('said', 384),\n"," ('bullying', 381),\n"," ('Gay', 378),\n"," ('radical', 377),\n"," (\"It's\", 375),\n"," ('going', 366),\n"," ('also', 366),\n"," ('good', 358),\n"," ('time', 352),\n"," ('man', 349),\n"," ('love', 347),\n"," ('We', 342),\n"," ('Islam', 342),\n"," ('It', 341),\n"," ('it.', 336),\n"," ('got', 334),\n"," ('fucking', 327),\n"," ('don’t', 326),\n"," ('funny', 324),\n"," ('many', 321),\n"," ('Why', 319),\n"," ('jokes.', 316),\n"," ('He', 312),\n"," ('someone', 309),\n"," ('makes', 309),\n"," ('shit', 308),\n"," ('way', 305),\n"," ('much', 301),\n"," ('No', 294),\n"," ('rape,', 292),\n"," ('How', 288),\n"," ('terrorism', 286),\n"," ('r', 284),\n"," ('bad', 280),\n"," (',', 280),\n"," ('every', 280),\n"," ('saying', 278),\n"," ('girls', 276),\n"," ('school', 276),\n"," ('stop', 274)]"]},"execution_count":50,"metadata":{},"output_type":"execute_result"}],"source":["# Count the frequency of each word\n","word_counts = Counter(words)\n","top_words = word_counts.most_common(100)\n","top_words"]},{"cell_type":"code","execution_count":51,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:30:57.947764Z","iopub.status.busy":"2023-03-04T08:30:57.947282Z","iopub.status.idle":"2023-03-04T08:30:58.196337Z","shell.execute_reply":"2023-03-04T08:30:58.195179Z","shell.execute_reply.started":"2023-03-04T08:30:57.947714Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Create a bar chart of the most common words\n","top_words = word_counts.most_common(10) # Change the number to show more/less words\n","x_values = [word[0] for word in top_words]\n","y_values = [word[1] for word in top_words]\n","plt.bar(x_values, y_values)\n","plt.xlabel('Word')\n","plt.ylabel('Frequency')\n","plt.title('Most Commonly Used Words')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["# Visualizations\n","---"]},{"cell_type":"markdown","metadata":{},"source":["### 1. Wordclouds"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["from wordcloud import WordCloud\n","\n","text = ' '.join([word for word in df['tweet_text']])\n","plt.figure(figsize=(20,15), facecolor='None')\n","wordcloud = WordCloud(max_words=1000, width=1600, height=800).generate(text)\n","plt.imshow(wordcloud, interpolation='bilinear')\n","plt.axis(\"off\")\n","plt.title('Most frequent words used in Tweets', fontsize=19)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### 2. Sentiment Analysis Plot"]},{"cell_type":"code","execution_count":53,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:31:09.801329Z","iopub.status.busy":"2023-03-04T08:31:09.800767Z","iopub.status.idle":"2023-03-04T08:31:16.302610Z","shell.execute_reply":"2023-03-04T08:31:16.301192Z","shell.execute_reply.started":"2023-03-04T08:31:09.801291Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","from textblob import TextBlob\n","\n","# perform sentiment analysis on each text in DataFrame\n","sentiment_scores = []\n","for text in df['tweet_text']:\n","    analysis = TextBlob(text)\n","    sentiment_scores.append((analysis.sentiment.polarity, analysis.sentiment.subjectivity))\n","\n","# create DataFrame with sentiment scores\n","sentiment_df = pd.DataFrame(sentiment_scores, columns=['polarity', 'subjectivity'])\n","\n","# plot distribution of sentiment scores\n","fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))\n","sentiment_df['polarity'].plot(kind='hist', ax=axes[0], title='Polarity')\n","sentiment_df['subjectivity'].plot(kind='hist', ax=axes[1], title='Subjectivity')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### 3. Named Entity Recognition (NER) Plot"]},{"cell_type":"code","execution_count":54,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:31:16.304215Z","iopub.status.busy":"2023-03-04T08:31:16.303856Z","iopub.status.idle":"2023-03-04T08:31:16.309183Z","shell.execute_reply":"2023-03-04T08:31:16.308037Z","shell.execute_reply.started":"2023-03-04T08:31:16.304181Z"},"trusted":true},"outputs":[],"source":["import spacy\n","from spacy import displacy"]},{"cell_type":"code","execution_count":55,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:31:16.312931Z","iopub.status.busy":"2023-03-04T08:31:16.312601Z","iopub.status.idle":"2023-03-04T08:31:16.979411Z","shell.execute_reply":"2023-03-04T08:31:16.978282Z","shell.execute_reply.started":"2023-03-04T08:31:16.312898Z"},"trusted":true},"outputs":[{"data":{"text/html":["<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">@RudhoeEnglish This is an \n","<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n","    ISIS\n","    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n","</mark>\n"," account pretending to be a \n","<mark class=\"entity\" style=\"background: #c887fb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n","    Kurdish\n","    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">NORP</span>\n","</mark>\n"," account.  Like \n","<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n","    Islam\n","    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n","</mark>\n",", it is all lies.</div></span>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"}],"source":["#sample text\n","text = df['tweet_text'].iloc[4]\n","\n","#load pre-trained NER model\n","nlp = spacy.load('en_core_web_sm')\n","\n","#perform named entity recognition\n","doc = nlp(text)\n","\n","#visualize named entities\n","displacy.render(doc, style='ent', jupyter=True)"]},{"cell_type":"markdown","metadata":{},"source":["### 4. Part-of-Speech (POS) Tagging Plot"]},{"cell_type":"code","execution_count":56,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:31:16.981406Z","iopub.status.busy":"2023-03-04T08:31:16.981045Z","iopub.status.idle":"2023-03-04T08:31:17.641352Z","shell.execute_reply":"2023-03-04T08:31:17.640525Z","shell.execute_reply.started":"2023-03-04T08:31:16.981371Z"},"trusted":true},"outputs":[{"data":{"text/html":["<span class=\"tex2jax_ignore\"><svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xml:lang=\"en\" id=\"c20dfa40c3e44d858e74893dd4ebc51b-0\" class=\"displacy\" width=\"1310\" height=\"227.0\" direction=\"ltr\" style=\"max-width: none; height: 227.0px; color: #000000; background: #ffffff; font-family: Arial; direction: ltr\">\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">Why</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">SCONJ</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"140\">is #</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"140\">AUX</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"230\">aussietv</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"230\">ADJ</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"320\">so</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"320\">ADV</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"410\">white? #</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"410\">ADJ</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"500\">MKR #</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"500\">NOUN</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"590\">theblock #</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"590\">NOUN</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"680\">ImACelebrityAU #</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"680\">NOUN</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"770\">today #</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"770\">NOUN</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"860\">sunrise #</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"860\">NOUN</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"950\">studio10 #</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"950\">ADJ</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1040\">Neighbours #</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1040\">PROPN</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1130\">WonderlandTen #</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1130\">PROPN</tspan>\n","</text>\n","\n","<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"137.0\">\n","    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1220\">etc</tspan>\n","    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1220\">X</tspan>\n","</text>\n","\n","<g class=\"displacy-arrow\">\n","    <path class=\"displacy-arc\" 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spacy.load('en_core_web_sm')\n","\n","#perform POS tagging\n","doc = nlp(text)\n","\n","#visualize POS tagging\n","displacy.render(doc, style='dep', jupyter=True, options={'distance': 90})"]},{"cell_type":"markdown","metadata":{},"source":["### 5. Topic Modeling Visualization"]},{"cell_type":"code","execution_count":57,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:31:17.643070Z","iopub.status.busy":"2023-03-04T08:31:17.642562Z","iopub.status.idle":"2023-03-04T08:31:28.537747Z","shell.execute_reply":"2023-03-04T08:31:28.536558Z","shell.execute_reply.started":"2023-03-04T08:31:17.643035Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: pyLDAvis in /opt/conda/lib/python3.7/site-packages (3.2.2)\n","Requirement already satisfied: funcy in /opt/conda/lib/python3.7/site-packages (from pyLDAvis) (1.18)\n","Requirement already satisfied: numexpr in /opt/conda/lib/python3.7/site-packages (from pyLDAvis) (2.8.4)\n","Requirement already satisfied: pandas>=0.17.0 in /opt/conda/lib/python3.7/site-packages (from pyLDAvis) (1.3.5)\n","Requirement already satisfied: joblib>=0.8.4 in /opt/conda/lib/python3.7/site-packages (from pyLDAvis) (1.2.0)\n","Requirement already satisfied: future in /opt/conda/lib/python3.7/site-packages (from pyLDAvis) (0.18.3)\n","Requirement already satisfied: jinja2>=2.7.2 in /opt/conda/lib/python3.7/site-packages (from pyLDAvis) (3.1.2)\n","Requirement already satisfied: scipy>=0.18.0 in /opt/conda/lib/python3.7/site-packages (from pyLDAvis) (1.7.3)\n","Requirement already satisfied: numpy>=1.9.2 in /opt/conda/lib/python3.7/site-packages (from pyLDAvis) (1.21.6)\n","Requirement already satisfied: wheel>=0.23.0 in /opt/conda/lib/python3.7/site-packages (from pyLDAvis) (0.38.4)\n","Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.7/site-packages (from jinja2>=2.7.2->pyLDAvis) (2.1.1)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.17.0->pyLDAvis) (2.8.2)\n","Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.17.0->pyLDAvis) (2022.7.1)\n","Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas>=0.17.0->pyLDAvis) (1.16.0)\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n","\u001b[0m"]}],"source":["!pip install pyLDAvis"]},{"cell_type":"code","execution_count":58,"metadata":{"_kg_hide-output":false,"execution":{"iopub.execute_input":"2023-03-04T08:31:28.540547Z","iopub.status.busy":"2023-03-04T08:31:28.540034Z","iopub.status.idle":"2023-03-04T08:31:44.610783Z","shell.execute_reply":"2023-03-04T08:31:44.609464Z","shell.execute_reply.started":"2023-03-04T08:31:28.540497Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.7/site-packages/pyLDAvis/_prepare.py:248: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n","  by='saliency', ascending=False).head(R).drop('saliency', 1)\n"]},{"data":{"text/html":["\n","<link rel=\"stylesheet\" type=\"text/css\" 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LDAvis(\"#\" + \"ldavis_el2401402063979149603377330049\", ldavis_el2401402063979149603377330049_data);\n","            })\n","         });\n","}\n","</script>"],"text/plain":["PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n","topic                                                \n","2     -0.269616 -0.005733       1        1  52.010695\n","6     -0.139981 -0.061642       2        1  11.788697\n","4     -0.121911 -0.069898       3        1   7.312488\n","3     -0.085546  0.058492       4        1   6.633752\n","7      0.059149  0.192594       5        1   4.952422\n","8     -0.013694  0.121480       6        1   4.947580\n","1      0.007868 -0.100931       7        1   4.521433\n","9      0.107191 -0.062451       8        1   2.754872\n","5      0.236652 -0.007125       9        1   2.730181\n","0      0.219888 -0.064786      10        1   2.347880, topic_info=           Term          Freq         Total Category  logprob  loglift\n","8069       rape   2393.000000   2393.000000  Default  30.0000  30.0000\n","1141        gay   1830.000000   1830.000000  Default  29.0000  29.0000\n","5833      jokes   1545.000000   1545.000000  Default  28.0000  28.0000\n","25            a  10532.000000  10532.000000  Default  27.0000  27.0000\n","20           is   6957.000000   6957.000000  Default  26.0000  26.0000\n","...         ...           ...           ...      ...      ...      ...\n","2603     making     41.111321    416.062129  Topic10  -5.5111   1.4371\n","4306  terrorist     40.462172    595.388467  Topic10  -5.5270   1.0628\n","25            a     53.152250  10532.655957  Topic10  -5.2542  -1.5374\n","5209    calling     27.043225    247.319585  Topic10  -5.9299   1.5384\n","20           is     27.107761   6957.147016  Topic10  -5.9275  -1.7960\n","\n","[627 rows x 6 columns], token_table=      Topic      Freq    Term\n","term                         \n","1823      1  0.026728      !!\n","1823      5  0.026728      !!\n","1823      9  0.962192      !!\n","8062      5  0.754996  #Islam\n","8062      9  0.241288  #Islam\n","...     ...       ...     ...\n","8         9  0.004362    your\n","8689      8  0.994045  you’re\n","2856      6  0.045822       …\n","2856      8  0.916448       …\n","2856     10  0.015274       …\n","\n","[1283 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[3, 7, 5, 4, 8, 9, 2, 10, 6, 1])"]},"execution_count":58,"metadata":{},"output_type":"execute_result"}],"source":["import gensim\n","import pyLDAvis \n","from pyLDAvis import *\n","import pyLDAvis.gensim as gensimvis\n","\n","# Preprocessing\n","tokens = [[word for word in sentence.split()] for sentence in df['tweet_text']]\n","dictionary = gensim.corpora.Dictionary(tokens)\n","corpus = [dictionary.doc2bow(token) for token in tokens]\n","\n","# Topic Modeling\n","num_topics = 10\n","lda_model = gensim.models.LdaModel(corpus=corpus, id2word=dictionary, num_topics=num_topics)\n","\n","# Visualization\n","pyLDAvis.enable_notebook()\n","vis = gensimvis.prepare(lda_model, corpus, dictionary)\n","vis"]},{"cell_type":"markdown","metadata":{},"source":["# Natural Language Processing\n","---"]},{"cell_type":"markdown","metadata":{},"source":["### 1. Data Cleaning"]},{"cell_type":"code","execution_count":59,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:31:44.613414Z","iopub.status.busy":"2023-03-04T08:31:44.612936Z","iopub.status.idle":"2023-03-04T08:31:44.622979Z","shell.execute_reply":"2023-03-04T08:31:44.621960Z","shell.execute_reply.started":"2023-03-04T08:31:44.613361Z"},"trusted":true},"outputs":[],"source":["# Clean the data\n","def clean_text(text):\n","    # Remove HTML tags\n","    text = re.sub('<.*?>', '', text)\n","    \n","    # Remove non-alphabetic characters and convert to lowercase\n","    text = re.sub('[^a-zA-Z]', ' ', text).lower()\n","    \n","    # Remove URLs, mentions, and hashtags from the text\n","    text = re.sub(r'http\\S+', '', text)\n","    text = re.sub(r'@\\S+', '', text)\n","    text = re.sub(r'#\\S+', '', text)\n","    \n","    # Tokenize the text\n","    words = nltk.word_tokenize(text)\n","    \n","    # Remove stopwords\n","    words = [w for w in words if w not in stopwords.words('english')]\n","    \n","    # Stem the words\n","    stemmer = PorterStemmer()\n","    words = [stemmer.stem(w) for w in words]\n","    \n","    # Join the words back into a string\n","    text = ' '.join(words)\n","    return text"]},{"cell_type":"code","execution_count":60,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:31:44.625041Z","iopub.status.busy":"2023-03-04T08:31:44.624419Z","iopub.status.idle":"2023-03-04T08:33:03.752337Z","shell.execute_reply":"2023-03-04T08:33:03.751170Z","shell.execute_reply.started":"2023-03-04T08:31:44.625002Z"},"trusted":true},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"22da0defd24e40568849d494a10bef84","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/20000 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["CPU times: user 1min 10s, sys: 8.52 s, total: 1min 19s\n","Wall time: 1min 19s\n"]}],"source":["%%time\n","\n","tqdm.pandas()\n","\n","df['cleaned_text'] = df['tweet_text'].progress_apply(clean_text)"]},{"cell_type":"markdown","metadata":{},"source":["### 2. Feature Extraction"]},{"cell_type":"code","execution_count":61,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:33:03.754168Z","iopub.status.busy":"2023-03-04T08:33:03.753793Z","iopub.status.idle":"2023-03-04T08:33:04.750773Z","shell.execute_reply":"2023-03-04T08:33:04.749577Z","shell.execute_reply.started":"2023-03-04T08:33:03.754112Z"},"trusted":true},"outputs":[],"source":["# Create the Bag of Words model\n","cv = CountVectorizer()\n","X = cv.fit_transform(df['cleaned_text']).toarray()\n","y = df['cyberbullying_type']"]},{"cell_type":"code","execution_count":62,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:33:04.753201Z","iopub.status.busy":"2023-03-04T08:33:04.752734Z","iopub.status.idle":"2023-03-04T08:33:08.015048Z","shell.execute_reply":"2023-03-04T08:33:08.013830Z","shell.execute_reply.started":"2023-03-04T08:33:04.753111Z"},"trusted":true},"outputs":[],"source":["# Split the data into training and testing sets\n","from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"]},{"cell_type":"markdown","metadata":{},"source":["# Classification Model\n","---"]},{"cell_type":"markdown","metadata":{},"source":["### 1. Logistic Regression Model"]},{"cell_type":"code","execution_count":63,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:33:08.017173Z","iopub.status.busy":"2023-03-04T08:33:08.016798Z","iopub.status.idle":"2023-03-04T08:33:08.022310Z","shell.execute_reply":"2023-03-04T08:33:08.021202Z","shell.execute_reply.started":"2023-03-04T08:33:08.017113Z"},"trusted":true},"outputs":[],"source":["from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import accuracy_score\n","# train a Logistic Regression Model\n","clf = LogisticRegression(max_iter = 1000)"]},{"cell_type":"code","execution_count":64,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:33:08.023913Z","iopub.status.busy":"2023-03-04T08:33:08.023600Z","iopub.status.idle":"2023-03-04T08:35:31.050908Z","shell.execute_reply":"2023-03-04T08:35:31.049635Z","shell.execute_reply.started":"2023-03-04T08:33:08.023881Z"},"trusted":true},"outputs":[{"data":{"text/plain":["LogisticRegression(max_iter=1000)"]},"execution_count":64,"metadata":{},"output_type":"execute_result"}],"source":["clf.fit(X_train, y_train)"]},{"cell_type":"markdown","metadata":{},"source":["### 2. Predictions"]},{"cell_type":"code","execution_count":65,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:35:31.057391Z","iopub.status.busy":"2023-03-04T08:35:31.056568Z","iopub.status.idle":"2023-03-04T08:35:31.492997Z","shell.execute_reply":"2023-03-04T08:35:31.491672Z","shell.execute_reply.started":"2023-03-04T08:35:31.057326Z"},"trusted":true},"outputs":[{"data":{"text/plain":["array(['gender', 'not_cyberbullying', 'gender', ..., 'gender',\n","       'not_cyberbullying', 'gender'], dtype=object)"]},"execution_count":65,"metadata":{},"output_type":"execute_result"}],"source":["# evaluate the classifier on the test set\n","y_pred = clf.predict(X_test)\n","y_pred"]},{"cell_type":"markdown","metadata":{},"source":["### 3. Accuracy Score"]},{"cell_type":"code","execution_count":66,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:35:31.495625Z","iopub.status.busy":"2023-03-04T08:35:31.494881Z","iopub.status.idle":"2023-03-04T08:35:31.511848Z","shell.execute_reply":"2023-03-04T08:35:31.510468Z","shell.execute_reply.started":"2023-03-04T08:35:31.495576Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy: 0.914\n"]}],"source":["acc = accuracy_score(y_test, y_pred)\n","print(\"Accuracy:\", acc)"]},{"cell_type":"markdown","metadata":{},"source":["### 4. Confusion Matrix"]},{"cell_type":"code","execution_count":67,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:35:31.514702Z","iopub.status.busy":"2023-03-04T08:35:31.513888Z","iopub.status.idle":"2023-03-04T08:35:31.561654Z","shell.execute_reply":"2023-03-04T08:35:31.560281Z","shell.execute_reply.started":"2023-03-04T08:35:31.514654Z"},"trusted":true},"outputs":[{"data":{"text/plain":["array([[1425,  174,    3],\n","       [  71, 1498,   31],\n","       [   9,   56,  733]])"]},"execution_count":67,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn.metrics import confusion_matrix\n","cm = confusion_matrix(y_test, y_pred)\n","cm"]},{"cell_type":"code","execution_count":68,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:35:31.565024Z","iopub.status.busy":"2023-03-04T08:35:31.564412Z","iopub.status.idle":"2023-03-04T08:35:31.872084Z","shell.execute_reply":"2023-03-04T08:35:31.871255Z","shell.execute_reply.started":"2023-03-04T08:35:31.564964Z"},"trusted":true},"outputs":[{"data":{"text/plain":["<AxesSubplot:>"]},"execution_count":68,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import seaborn as sns\n","sns.heatmap(cm, annot=True)"]},{"cell_type":"markdown","metadata":{},"source":["### 5. Classification Report"]},{"cell_type":"code","execution_count":69,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T08:35:56.226274Z","iopub.status.busy":"2023-03-04T08:35:56.225811Z","iopub.status.idle":"2023-03-04T08:35:56.321001Z","shell.execute_reply":"2023-03-04T08:35:56.319853Z","shell.execute_reply.started":"2023-03-04T08:35:56.226232Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["                   precision    recall  f1-score   support\n","\n","           gender       0.95      0.89      0.92      1602\n","not_cyberbullying       0.87      0.94      0.90      1600\n","         religion       0.96      0.92      0.94       798\n","\n","         accuracy                           0.91      4000\n","        macro avg       0.92      0.91      0.92      4000\n","     weighted avg       0.92      0.91      0.91      4000\n","\n"]}],"source":["from sklearn.metrics import classification_report\n","report = classification_report(y_test, y_pred)\n","print(report)"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"}},"nbformat":4,"nbformat_minor":4}