{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **NATURAL LANGUAGE PROCESSING**\n\n**Natural Language Processing and Machine Learning make it possible to build robust models with the storage capacity and processing power available to us today. Natural Language Processing concepts deal with processing human langauge while discovering patterns, relationships and, semantics present in large amounts of data.**\n\n![nlp-important-use-cases-min.png](attachment:nlp-important-use-cases-min.png)","metadata":{},"attachments":{"nlp-important-use-cases-min.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# **Description**\n\n**This is a starter notebook for NLP in text processing. It covers Data Preprocessing and Feature Engineering in comprehensive details. The code is implemented in popular NLP libraries namely NLTK, SpaCy, and occasionaly TextBlob. Please upvote and share the notebook if you found it helpful in any way. Thank you in advance!.**\n\n![0_zKRz1UgqpOZ4bvuA-min.png](attachment:0_zKRz1UgqpOZ4bvuA-min.png)![1_ax2uBqfp963n4PQVqmGplQ-min.png](attachment:1_ax2uBqfp963n4PQVqmGplQ-min.png)![textblob-logo-min.png](attachment:textblob-logo-min.png)","metadata":{},"attachments":{"0_zKRz1UgqpOZ4bvuA-min.png":{"image/png":"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"},"1_ax2uBqfp963n4PQVqmGplQ-min.png":{"image/png":"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**Content**\n\n* **ENVIRONMENT SETUP**\n * Install Packages\n * Import Packages\n \n\n* **DATA PREPROCESSING**\n * Data Loading\n * Preliminary Analysis\n * Sentence Tokenization\n * Word Tokenization\n * Stopword Removal\n * Removal of Tags\n * Delimiter Removal\n * Spell Check \n * Stemming\n * Lemmatization\n \n \n* **FEATURE ENGINEERING**\n * Encoding \n * POS Tagger\n * N-GRAM\n * Bag Of Words\n * TF\n * TF-IDF\n * Dependency Parser\n * Named Entity Recognition\n * Word Embedding\n * Sentiment Analysis\n * Subjectivity Detection","metadata":{}},{"cell_type":"markdown","source":"# **ENVIRONMENT SETUP**","metadata":{}},{"cell_type":"markdown","source":"# **Install Packages**","metadata":{}},{"cell_type":"code","source":"!pip install -q autocorrect","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-08-06T01:01:49.316716Z","iopub.execute_input":"2022-08-06T01:01:49.317040Z","iopub.status.idle":"2022-08-06T01:01:59.614228Z","shell.execute_reply.started":"2022-08-06T01:01:49.317010Z","shell.execute_reply":"2022-08-06T01:01:59.613378Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
🌟 SpaCy Version 3.0!
","metadata":{}},{"cell_type":"code","source":"!pip install -q spacy==3.0.5","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-08-06T01:01:59.617510Z","iopub.execute_input":"2022-08-06T01:01:59.618163Z","iopub.status.idle":"2022-08-06T01:02:24.092732Z","shell.execute_reply.started":"2022-08-06T01:01:59.618125Z","shell.execute_reply":"2022-08-06T01:02:24.091744Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!python -m spacy download en_core_web_sm\n!python -m spacy download en_core_web_lg","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-08-06T01:03:07.552559Z","iopub.execute_input":"2022-08-06T01:03:07.552883Z","iopub.status.idle":"2022-08-06T01:03:23.739822Z","shell.execute_reply.started":"2022-08-06T01:03:07.552853Z","shell.execute_reply":"2022-08-06T01:03:23.738869Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install -q spacytextblob","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-08-06T01:03:29.280329Z","iopub.execute_input":"2022-08-06T01:03:29.280681Z","iopub.status.idle":"2022-08-06T01:03:36.291580Z","shell.execute_reply.started":"2022-08-06T01:03:29.280646Z","shell.execute_reply":"2022-08-06T01:03:36.290734Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Import Packages**","metadata":{}},{"cell_type":"code","source":"import warnings\nimport numpy as np \nimport pandas as pd \nimport os\nimport re\nimport nltk\nfrom nltk.corpus import abc\nfrom nltk.tokenize import sent_tokenize, word_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.stem.porter import *\nfrom nltk.stem.snowball import *\nfrom nltk.util import ngrams\nimport string\nimport spacy\nfrom spacy import displacy\nfrom spacytextblob.spacytextblob import SpacyTextBlob\nfrom autocorrect import Speller\nfrom sklearn import preprocessing\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.decomposition import PCA\nfrom wordcloud import WordCloud, STOPWORDS\nimport gensim\nfrom gensim.models.word2vec import Text8Corpus\nfrom gensim.models import KeyedVectors\nfrom gensim.scripts.glove2word2vec import glove2word2vec\nfrom nltk.sentiment import SentimentIntensityAnalyzer\nfrom textblob import TextBlob\nfrom IPython.core.display import HTML\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-08-06T01:03:41.328494Z","iopub.execute_input":"2022-08-06T01:03:41.328857Z","iopub.status.idle":"2022-08-06T01:03:46.258341Z","shell.execute_reply.started":"2022-08-06T01:03:41.328822Z","shell.execute_reply":"2022-08-06T01:03:46.257435Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **DATA PREPROCESSING**","metadata":{}},{"cell_type":"markdown","source":"# **Data Loading**","metadata":{}},{"cell_type":"code","source":"for dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-08-06T01:03:51.413512Z","iopub.execute_input":"2022-08-06T01:03:51.413857Z","iopub.status.idle":"2022-08-06T01:03:51.435450Z","shell.execute_reply.started":"2022-08-06T01:03:51.413826Z","shell.execute_reply":"2022-08-06T01:03:51.434578Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')\nprint('Total number of entries in the train dataset are:', len(data))\ndata.head()","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","execution":{"iopub.status.busy":"2022-08-06T01:04:02.890423Z","iopub.execute_input":"2022-08-06T01:04:02.890754Z","iopub.status.idle":"2022-08-06T01:04:02.949377Z","shell.execute_reply.started":"2022-08-06T01:04:02.890723Z","shell.execute_reply":"2022-08-06T01:04:02.948695Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Preliminary Analysis**","metadata":{}},{"cell_type":"code","source":"data.shape","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:04:07.711917Z","iopub.execute_input":"2022-08-06T01:04:07.712248Z","iopub.status.idle":"2022-08-06T01:04:07.718676Z","shell.execute_reply.started":"2022-08-06T01:04:07.712218Z","shell.execute_reply":"2022-08-06T01:04:07.717293Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:04:08.300249Z","iopub.execute_input":"2022-08-06T01:04:08.300607Z","iopub.status.idle":"2022-08-06T01:04:08.316710Z","shell.execute_reply.started":"2022-08-06T01:04:08.300576Z","shell.execute_reply":"2022-08-06T01:04:08.315993Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.dtypes","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:04:08.864339Z","iopub.execute_input":"2022-08-06T01:04:08.864678Z","iopub.status.idle":"2022-08-06T01:04:08.871594Z","shell.execute_reply.started":"2022-08-06T01:04:08.864645Z","shell.execute_reply":"2022-08-06T01:04:08.870677Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:04:09.514675Z","iopub.execute_input":"2022-08-06T01:04:09.514996Z","iopub.status.idle":"2022-08-06T01:04:09.524692Z","shell.execute_reply.started":"2022-08-06T01:04:09.514969Z","shell.execute_reply":"2022-08-06T01:04:09.523661Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.isna()","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:04:10.371889Z","iopub.execute_input":"2022-08-06T01:04:10.372213Z","iopub.status.idle":"2022-08-06T01:04:10.389715Z","shell.execute_reply.started":"2022-08-06T01:04:10.372185Z","shell.execute_reply":"2022-08-06T01:04:10.388752Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = data[['id', 'text']]\ndata","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:04:11.133786Z","iopub.execute_input":"2022-08-06T01:04:11.134104Z","iopub.status.idle":"2022-08-06T01:04:11.148424Z","shell.execute_reply.started":"2022-08-06T01:04:11.134075Z","shell.execute_reply":"2022-08-06T01:04:11.147516Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"text = data['text']\ntext","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:04:14.272110Z","iopub.execute_input":"2022-08-06T01:04:14.272455Z","iopub.status.idle":"2022-08-06T01:04:14.279527Z","shell.execute_reply.started":"2022-08-06T01:04:14.272424Z","shell.execute_reply":"2022-08-06T01:04:14.278692Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Sentence Tokenization**\n\nFor any corpus, we first divide a huge entity into smaller entities so that they can be treated individually. Tokenization also does a similar task but upon sentences in text. First, the text is broken down into sentences and that is further broken down into words. The input is given as text or a corpus. The output generates a list of sentences. For example, in the text, \"I love dogs. I have a dog\", the output is [\"I love dogs,” I have a dog”]\n\n![Screenshot%20%2868%29.png](attachment:Screenshot%20%2868%29.png)","metadata":{},"attachments":{"Screenshot%20%2868%29.png":{"image/png":"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"}}},{"cell_type":"code","source":"# sents1 = sent_tokenize() # tokenize the 1000th text\n# print(f'Sentence Tokenization using NLTK: \\n {text[1000]} => {sents1}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:04:15.734560Z","iopub.execute_input":"2022-08-06T01:04:15.734881Z","iopub.status.idle":"2022-08-06T01:04:15.752830Z","shell.execute_reply.started":"2022-08-06T01:04:15.734851Z","shell.execute_reply":"2022-08-06T01:04:15.751969Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Word Tokenization**\n\nWord tokenization is the same as sentence tokenization. But, rather than applying it to\nsentences, it is used on words so that individual words are separated as items in a\nlist. For example, in the sentence, \"Chennai is humid,” the result is [\"Chennai,” “is,”\n“humid”].\n\n![fig2-min-min.png](attachment:fig2-min-min.png)","metadata":{},"attachments":{"fig2-min-min.png":{"image/png":"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"}}},{"cell_type":"code","source":"# words1 = word_tokenize() # tokenize the first sentence\n# print(f'Word Tokenization using NLTK: \\n {sents1[0]} => {words1}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:07:18.266020Z","iopub.execute_input":"2022-08-06T01:07:18.266374Z","iopub.status.idle":"2022-08-06T01:07:18.271822Z","shell.execute_reply.started":"2022-08-06T01:07:18.266340Z","shell.execute_reply":"2022-08-06T01:07:18.270769Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sp = spacy.load('en_core_web_sm')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:06:17.292878Z","iopub.execute_input":"2022-08-06T01:06:17.293233Z","iopub.status.idle":"2022-08-06T01:06:17.796178Z","shell.execute_reply.started":"2022-08-06T01:06:17.293200Z","shell.execute_reply":"2022-08-06T01:06:17.795264Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f'Word Tokenization using SpaCy: \\n\\n{sp(text[0])} =>\\n')\n\nwords2 = sp(text[0])\nfor word in words2:\n print(word)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:06:18.023886Z","iopub.execute_input":"2022-08-06T01:06:18.024168Z","iopub.status.idle":"2022-08-06T01:06:18.059746Z","shell.execute_reply.started":"2022-08-06T01:06:18.024140Z","shell.execute_reply":"2022-08-06T01:06:18.059049Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Stopword Removal**\n\nThe dataset may contain words like ‘after,’ ‘every’ and ‘I.’ These words are not relevant to important NLP applications like the sentiment detection process. Thereby, these words can be deleted to minimize the burden on the system.\n\n![stopwords-min.jpg](attachment:stopwords-min.jpg)","metadata":{},"attachments":{"stopwords-min.jpg":{"image/jpeg":"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"}}},{"cell_type":"code","source":"nltk.download('stopwords')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:06:20.470063Z","iopub.execute_input":"2022-08-06T01:06:20.470487Z","iopub.status.idle":"2022-08-06T01:06:20.764854Z","shell.execute_reply.started":"2022-08-06T01:06:20.470452Z","shell.execute_reply":"2022-08-06T01:06:20.763959Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"words1 = ['#',\n 'OVOFest',\n 'Drake',\n 'straight',\n 'body',\n 'bagging',\n 'Meek',\n 'on',\n 'that',\n 'OVO',\n 'stage',\n '.']","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:10:24.446355Z","iopub.execute_input":"2022-08-06T01:10:24.446677Z","iopub.status.idle":"2022-08-06T01:10:24.451236Z","shell.execute_reply.started":"2022-08-06T01:10:24.446650Z","shell.execute_reply":"2022-08-06T01:10:24.450160Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tokens1 = [word for word in words1 if word not in stopwords.words('english')] \nprint(f'Stopword Removal using NLTK: \\n{words1} => {tokens1}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:08:13.478649Z","iopub.execute_input":"2022-08-06T01:08:13.478974Z","iopub.status.idle":"2022-08-06T01:08:13.488890Z","shell.execute_reply.started":"2022-08-06T01:08:13.478944Z","shell.execute_reply":"2022-08-06T01:08:13.487980Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"spacy_stopwords = sp.Defaults.stop_words\nprint(f'SpaCy Stopwords: \\n{spacy_stopwords}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:08:15.199907Z","iopub.execute_input":"2022-08-06T01:08:15.200231Z","iopub.status.idle":"2022-08-06T01:08:15.205061Z","shell.execute_reply.started":"2022-08-06T01:08:15.200202Z","shell.execute_reply":"2022-08-06T01:08:15.204146Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tokens2 = [word for word in words2 if word not in spacy_stopwords] \nprint(f'Stopword Removal using SpaCy: \\n{words2} => {tokens2}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:09:06.350163Z","iopub.execute_input":"2022-08-06T01:09:06.350538Z","iopub.status.idle":"2022-08-06T01:09:06.355821Z","shell.execute_reply.started":"2022-08-06T01:09:06.350506Z","shell.execute_reply":"2022-08-06T01:09:06.354934Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Delimiter Removal**\n\nDelimiters are removed to reduce the size of the dataset as they do not supply any vital information in some cases. A Few delimiters are question marks (?), full stops (.), and exclamation marks (!). For example, after delimiter removal the sentence 'I am cold!' becomes 'I am cold'. 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"}}},{"cell_type":"code","source":"from string import punctuation\nprint(f'Delimiters in English: \\n{punctuation}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:09:08.544037Z","iopub.execute_input":"2022-08-06T01:09:08.544473Z","iopub.status.idle":"2022-08-06T01:09:08.549711Z","shell.execute_reply.started":"2022-08-06T01:09:08.544426Z","shell.execute_reply":"2022-08-06T01:09:08.547962Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tokens1 = [word for word in words1 if word not in punctuation]\nprint(f'Delimiter Removal: \\n{words1} => {tokens1}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:09:11.160418Z","iopub.execute_input":"2022-08-06T01:09:11.160749Z","iopub.status.idle":"2022-08-06T01:09:11.166884Z","shell.execute_reply.started":"2022-08-06T01:09:11.160719Z","shell.execute_reply":"2022-08-06T01:09:11.165797Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Removal of Tags**\n\nDuring web scraping, the data is scraped from web pages residing on the website, and they contain HTML tags. These tags do not provide any necessary information and hence, can be erased. For example, a tag like < body > (Body Tag) is deleted.\n\n![SEO-Meta-Tags-min.jpg](attachment:SEO-Meta-Tags-min.jpg)","metadata":{},"attachments":{"SEO-Meta-Tags-min.jpg":{"image/jpeg":"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= \" Natural Language Processing \"\nremove_html = re.compile('<.*?>')\n\nprint(f\"Removing HTML tags: \\n{sent_with_html} => {re.sub(remove_html, '', sent_with_html).strip()}\")","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:09:17.788459Z","iopub.execute_input":"2022-08-06T01:09:17.789130Z","iopub.status.idle":"2022-08-06T01:09:17.802646Z","shell.execute_reply.started":"2022-08-06T01:09:17.789083Z","shell.execute_reply":"2022-08-06T01:09:17.799366Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Spell Check**\n\nWhile communicating online, people use shorthand, urban dictionary words, and slang in their language. Standard dictionaries do not hold these words, so lookups into these dictionaries lead to inconsistency in data. Therefore it is essential to correct spelling.\n\n![1_6lG-4g_jIIco25FPyRa1AQ-min.png](attachment:1_6lG-4g_jIIco25FPyRa1AQ-min.png)","metadata":{},"attachments":{"1_6lG-4g_jIIco25FPyRa1AQ-min.png":{"image/png":"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"}}},{"cell_type":"code","source":"spell = Speller()\n\nprint('Spelling Correction:\\n')\nfor token in tokens1:\n print(f'{token} => {spell(token)}')\n","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:09:22.751616Z","iopub.execute_input":"2022-08-06T01:09:22.751942Z","iopub.status.idle":"2022-08-06T01:09:22.950606Z","shell.execute_reply.started":"2022-08-06T01:09:22.751913Z","shell.execute_reply":"2022-08-06T01:09:22.949859Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"spell = Speller()\nmisspelled = ['Calandar', 'neccessary', 'recieve', 'adress', 'misteak']\n\nprint('Tokens before and after Spelling Correction (Own Example):\\n')\nfor token in misspelled:\n print(f'{token} : {spell(token)}')\n","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:09:37.014094Z","iopub.execute_input":"2022-08-06T01:09:37.014446Z","iopub.status.idle":"2022-08-06T01:09:37.154561Z","shell.execute_reply.started":"2022-08-06T01:09:37.014414Z","shell.execute_reply":"2022-08-06T01:09:37.153667Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Stemming**\n\nStemming applies algorithmic rules to extract the stem out of the derived word simply. The words produced by this step do not have any essential meaning, but they are simply a bunch of letters put together without affixes. For example, the word “beautiful” is stemmed to “Beauti”.\n\n![09-min.jpg](attachment:09-min.jpg)","metadata":{},"attachments":{"09-min.jpg":{"image/jpeg":"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= PorterStemmer()\nstemWords1 = [porterStemmer.stem(word) for word in tokens1]\n\nprint(f'Tokens after Stemming using Porter Stemmer: \\n{stemWords1}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:10:51.472141Z","iopub.execute_input":"2022-08-06T01:10:51.472540Z","iopub.status.idle":"2022-08-06T01:10:51.478661Z","shell.execute_reply.started":"2022-08-06T01:10:51.472506Z","shell.execute_reply":"2022-08-06T01:10:51.477260Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"snowballStemmer = SnowballStemmer('english')\nstemWords2 = [snowballStemmer.stem(word) for word in tokens1]\n\nprint(f'Tokens after Stemming using Snowball Stemmer: \\n{stemWords2}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:10:57.032206Z","iopub.execute_input":"2022-08-06T01:10:57.032628Z","iopub.status.idle":"2022-08-06T01:10:57.044150Z","shell.execute_reply.started":"2022-08-06T01:10:57.032594Z","shell.execute_reply":"2022-08-06T01:10:57.043235Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Lemmatization**\n\nLemmatization is similar to stemming but it add context to bring out the true meaning. It groups inflected forms of words to be interpretd as a single root word. For example, the word “beautiful” is stemmed to “Beauty” unlike “Beauti”.\n\n![stemvslemma.png](attachment:ec28f8ab-2d2f-4c47-a82f-27ebc94d3c0e.png)","metadata":{},"attachments":{"ec28f8ab-2d2f-4c47-a82f-27ebc94d3c0e.png":{"image/png":"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"}}},{"cell_type":"code","source":"wordNetLemmatizer = WordNetLemmatizer()\nlemmaWords1 = [wordNetLemmatizer.lemmatize(word) for word in tokens1]\n\nprint(f'Tokens after Lemmatization using WordNet Lemmatizer: \\n{tokens1} => {lemmaWords1}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:11:30.619918Z","iopub.execute_input":"2022-08-06T01:11:30.620245Z","iopub.status.idle":"2022-08-06T01:11:32.314548Z","shell.execute_reply.started":"2022-08-06T01:11:30.620216Z","shell.execute_reply":"2022-08-06T01:11:32.313096Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"lemmaWords2 = [word.lemma_ for word in tokens2]\n\nprint(f'Tokens after Lemmatization using SpaCy Lemmatization: \\n{tokens2} => {lemmaWords2}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:11:35.048761Z","iopub.execute_input":"2022-08-06T01:11:35.049096Z","iopub.status.idle":"2022-08-06T01:11:35.056439Z","shell.execute_reply.started":"2022-08-06T01:11:35.049063Z","shell.execute_reply":"2022-08-06T01:11:35.055597Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **FEATURE ENGINEERING**","metadata":{}},{"cell_type":"markdown","source":"# **Encoding**\n\nEncoding is the process of encrypting data in a format that computers can understand. Humans comprehend natural language. However, a machine is capable of decoding only 0s and 1s. Encoding converts text to digits. For example, the words 'positive' and 'negative' are mapped to the numbers '0' and '1'.\n\n![1_Yp6r7m82IoSnnZDPpDpYNw-min.png](attachment:521f4c95-586c-4a8a-ba38-b54fbcf99120.png)","metadata":{},"attachments":{"521f4c95-586c-4a8a-ba38-b54fbcf99120.png":{"image/png":"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animals = ['dog', 'cat', 'mouse', 'dog', 'lion', 'lion', 'mouse', 'tiger', 'rat', 'dog']\n\n# label_encoder = preprocessing.LabelEncoder()\n# encoded_values = # lable encode results\n# data = pd.DataFrame({'Labels' : animals, 'Label Encoder Values' : encoded_values})\n\n# print(\"Label Encoder\")\n# data.style.background_gradient(cmap = 'BrBG')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:12:55.680530Z","iopub.execute_input":"2022-08-06T01:12:55.681004Z","iopub.status.idle":"2022-08-06T01:12:55.686441Z","shell.execute_reply.started":"2022-08-06T01:12:55.680951Z","shell.execute_reply":"2022-08-06T01:12:55.685212Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# animals = np.array(['dog', 'cat', 'mouse', 'dog', 'lion', 'lion', 'mouse', 'tiger', 'rat', 'dog'])\n\n# ohe = preprocessing.OneHotEncoder()\n# result = # one hot encoder results \n\n# data = pd.DataFrame(result.astype(int))\n# data['Labels'] = animals\n\n# print(\"One Hot Encoder\")\n# data.style.background_gradient(cmap = 'Wistia')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:13:26.431826Z","iopub.execute_input":"2022-08-06T01:13:26.432139Z","iopub.status.idle":"2022-08-06T01:13:26.436626Z","shell.execute_reply.started":"2022-08-06T01:13:26.432110Z","shell.execute_reply":"2022-08-06T01:13:26.435578Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **POS Tagger**\n\nPOS tagger is parts of speech tagger that is an in-built function found in a standard library. It tags the words in the sentences according to the grammar of the langauge. For example, in the text, “The pizza was disgusting but the location was beautiful”, the result after implementing POS tagger will be [“The [DT]”, “pizza [NN]”, “is [VB]”, “disgusting [VBG]”, “but [CC]”, “the [DT]”, “location [NN]”, “was [VBD], “beautiful [JJ]].\n\n![1_fRjvBbgzo90x0MZdXZT82A%20%281%29-min.png](attachment:1_fRjvBbgzo90x0MZdXZT82A%20%281%29-min.png)","metadata":{},"attachments":{"1_fRjvBbgzo90x0MZdXZT82A%20%281%29-min.png":{"image/png":"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"}}},{"cell_type":"code","source":"tagged_tokens1 = nltk.pos_tag(tokens1)\n\nprint(f'POS tagging using NLTK: \\n{tokens1} => {tagged_tokens1}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:13:37.069964Z","iopub.execute_input":"2022-08-06T01:13:37.070304Z","iopub.status.idle":"2022-08-06T01:13:37.178366Z","shell.execute_reply.started":"2022-08-06T01:13:37.070258Z","shell.execute_reply":"2022-08-06T01:13:37.177304Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tagged_tokens2 = [word.pos_ for word in tokens2]\n\nprint('POS tagging using SpaCy: \\n')\nfor i in range(len(tagged_tokens2)):\n print(f'{tokens2[i]} : {tagged_tokens2[i]}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:13:42.932781Z","iopub.execute_input":"2022-08-06T01:13:42.933102Z","iopub.status.idle":"2022-08-06T01:13:42.941181Z","shell.execute_reply.started":"2022-08-06T01:13:42.933073Z","shell.execute_reply":"2022-08-06T01:13:42.940236Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **N-Gram**\n\nN-gram is a language model widely used in NLP and is applied to statistical problems involving text and audio. It is a probabilistic model that predicts the next series of words. For example, in the sentence, “The movie was boring.” Unigram processes the text as [“The”, “movie”, “was”, “boring”]. Bi-gram processes the text as [“The movie”, “movie was”, “was boring”]. Tri-gram processes the text as [“The movie was”, “movie was boring”]\n\n![7356.1569499094-min.png](attachment:7356.1569499094-min.png)","metadata":{},"attachments":{"7356.1569499094-min.png":{"image/png":"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= ngrams(tokens1, 2)\nn_grams1 = [ ' '.join(grams) for grams in n_grams1]\n\nprint(f'N-Gram using NLTK (n = 2): \\n{tokens1} => {n_grams1}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:13:54.951892Z","iopub.execute_input":"2022-08-06T01:13:54.952219Z","iopub.status.idle":"2022-08-06T01:13:54.958653Z","shell.execute_reply.started":"2022-08-06T01:13:54.952189Z","shell.execute_reply":"2022-08-06T01:13:54.956566Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"n_gram_finder = nltk.collocations.TrigramCollocationFinder.from_words(tokens1)\n\nprint(f'Most Common N-Gram Finder using NLTK (n = 3): \\n{tokens1} => {n_gram_finder.ngram_fd.most_common(2)}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:14:02.373025Z","iopub.execute_input":"2022-08-06T01:14:02.373373Z","iopub.status.idle":"2022-08-06T01:14:02.381152Z","shell.execute_reply.started":"2022-08-06T01:14:02.373333Z","shell.execute_reply":"2022-08-06T01:14:02.379999Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Bag of Words**\n\nThe bag of words carries out sentence tokenization and word tokenization. After that, it counts the number of appearances of each word. For example, in a sentence, “It is nice but horrid, and that’s not a nice thing.” The word “nice” is extracted and countered with two occurrences.\n\n![image-20190906164045-2-min.jpeg](attachment:image-20190906164045-2-min.jpeg)","metadata":{},"attachments":{"image-20190906164045-2-min.jpeg":{"image/jpeg":"/9j/4AAQSkZJRgABAQEAYABgAAD/4R+iRXhpZgAATU0AKgAAAAgABgALAAIAAAAmAAAIYgESAAMAAAABAAEAAAExAAIAAAAmAAAIiAEyAAIAAAAUAAAIrodpAAQAAAABAAAIwuocAAcAAAgMAAAAVgAAEUYc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFdpbmRvd3MgUGhvdG8gRWRpdG9yIDEwLjAuMTAwMTEuMTYzODQAV2luZG93cyBQaG90byBFZGl0b3IgMTAuMC4xMDAxMS4xNjM4NAAyMDIxOjAzOjI5IDE1OjQ3OjMxAAAGkAMAAgAAABQAABEckAQAAgAAABQAABEwkpEAAgAAAAMwMAAAkpIAAgAAAAMwMAAAoAEAAwAAAAEAAQAA6hwABwAACAwAAAkQAAAAABzqAAAACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA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"}}},{"cell_type":"code","source":"word_count = {}\n\nfor word in tokens1:\n \n if word not in word_count.keys():\n word_count[word] = 1\n else:\n word_count[word] += 1\n \nprint(f'Bag of Words: \\n{tokens1} => {word_count}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:14:15.219787Z","iopub.execute_input":"2022-08-06T01:14:15.220115Z","iopub.status.idle":"2022-08-06T01:14:15.225778Z","shell.execute_reply.started":"2022-08-06T01:14:15.220084Z","shell.execute_reply":"2022-08-06T01:14:15.224769Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"stopwords = set(STOPWORDS)\n\nwc = WordCloud(width = 900, height = 500, background_color = 'white', random_state = 10).generate(text[1])\n\nplt.title('Word Cloud for Bag Of Words')\nplt.imshow(wc)\nplt.axis('off')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:14:17.840281Z","iopub.execute_input":"2022-08-06T01:14:17.840857Z","iopub.status.idle":"2022-08-06T01:14:18.229112Z","shell.execute_reply.started":"2022-08-06T01:14:17.840815Z","shell.execute_reply":"2022-08-06T01:14:18.228217Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Term Frequency**\n\nTF – Term Frequency is described as the number of times that a term occurs in a document. It considers all the terms of equal importance. For example, the word “Fruit” appears five times in a document of 100 words, then the TF for “Fruit” is 5/100 = 0.05.\n\n![term_frequency_after_stopword_removal-min.png](attachment:term_frequency_after_stopword_removal-min.png)","metadata":{},"attachments":{"term_frequency_after_stopword_removal-min.png":{"image/png":"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"}}},{"cell_type":"code","source":"def color(val):\n \n color = 'mediumaquamarine' if val > 0 else ''\n return 'background-color: %s' % color","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:15:50.871897Z","iopub.execute_input":"2022-08-06T01:15:50.872233Z","iopub.status.idle":"2022-08-06T01:15:50.877033Z","shell.execute_reply.started":"2022-08-06T01:15:50.872203Z","shell.execute_reply":"2022-08-06T01:15:50.875909Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"count_vectorizer = CountVectorizer()\ntext_list = list(text[0:10])\n\ntf = count_vectorizer.fit_transform(text_list)\n\ntf_feature_names = count_vectorizer.get_feature_names()\n\nprint('Term Frequency of Document')\ndf = pd.DataFrame(tf.toarray(), columns = tf_feature_names) / len(tf_feature_names)\ndf.style.set_caption(\"Term Frequency of Document\")\ndf.style.applymap(color)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:15:52.585804Z","iopub.execute_input":"2022-08-06T01:15:52.586170Z","iopub.status.idle":"2022-08-06T01:15:52.767375Z","shell.execute_reply.started":"2022-08-06T01:15:52.586136Z","shell.execute_reply":"2022-08-06T01:15:52.766388Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Term Frequency - Inverse Document Frequency**\n\nTF-IDF – Term Frequency-Inverse Document Frequency is described as the importance of a word in a document, which is proportional to the number of times the word appears in the document. For example, the word “Fruit” appears in 100 of 10000 documents and the term frequency is 5 then the TF-IDF is 0.05 * log(10000/100) = 5 * 2 = 10.\n\n![0_7r2GKRepjh5Fl41r-min.png](attachment:0_7r2GKRepjh5Fl41r-min.png)","metadata":{},"attachments":{"0_7r2GKRepjh5Fl41r-min.png":{"image/png":"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"}}},{"cell_type":"code","source":"tfidf_vectorizer = TfidfVectorizer()\ntext_list = list(text[0:10])\n\ntfidf = tfidf_vectorizer.fit_transform(text_list)\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names()\n\nprint('Term Frequency - Inverse Document Frequency of Document')\ndf = pd.DataFrame(tfidf.toarray(), columns = tfidf_feature_names)\ndf.style.set_caption(\"Term Frequency - Inverse Document Frequency of Document\")\ndf.style.applymap(color)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:16:25.985111Z","iopub.execute_input":"2022-08-06T01:16:25.985468Z","iopub.status.idle":"2022-08-06T01:16:26.156178Z","shell.execute_reply.started":"2022-08-06T01:16:25.985435Z","shell.execute_reply":"2022-08-06T01:16:26.155359Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Dependency Parser**\n\nStanford dependency parser establishes the relationship between entities in the language using grammatical rules. The output of the parser is a tree structure that is annotated. For example, in the sentence “The funny boy joked,” “funny” is an adjective for the noun “boy.”\n\n![Syntax+Parsing+with+CoreNLP+and+NLTK.png](attachment:Syntax+Parsing+with+CoreNLP+and+NLTK.png)","metadata":{},"attachments":{"Syntax+Parsing+with+CoreNLP+and+NLTK.png":{"image/png":"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"}}},{"cell_type":"code","source":"grammar = \"NP: {
?*}\"\n\nparser = nltk.RegexpParser(grammar)\nparse_tree = parser.parse(tagged_tokens1)\n\nprint(f'Dependency Parse Tree using NLTK: \\n\\n{sents1[0]} => \\n\\n{parse_tree}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:16:39.641931Z","iopub.execute_input":"2022-08-06T01:16:39.642253Z","iopub.status.idle":"2022-08-06T01:16:39.649680Z","shell.execute_reply.started":"2022-08-06T01:16:39.642223Z","shell.execute_reply":"2022-08-06T01:16:39.648785Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Dependency Parse Tree using SpaCy')\n\ndisplacy.render(sp(text[1000]), style = \"dep\", jupyter = True, options = {\"distance\" : 100})","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:16:43.580127Z","iopub.execute_input":"2022-08-06T01:16:43.580470Z","iopub.status.idle":"2022-08-06T01:16:43.604916Z","shell.execute_reply.started":"2022-08-06T01:16:43.580439Z","shell.execute_reply":"2022-08-06T01:16:43.604021Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Named Entity Recognition**\n\nNER - Named Entity Recognition is the process of extracting proper nouns or proper noun phrases. For example, in the sentence 'Robert is interested in Amazon', the entities 'Robert' (Name) and 'Amazon' (Organization) are selected.\n\n![do-biomedical-ner-with-nlp-min.png](attachment:do-biomedical-ner-with-nlp-min.png)","metadata":{},"attachments":{"do-biomedical-ner-with-nlp-min.png":{"image/png":"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"}}},{"cell_type":"code","source":"ner_chunk = nltk.ne_chunk(tagged_tokens1)\n\nprint(f'Named Entity Recognition using NLTK: \\n\\n{sents1[0]} =>\\n')\n\nfor x in str(ner_chunk).split('\\n'):\n if '/NN' in x:\n print(x)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:16:56.905589Z","iopub.execute_input":"2022-08-06T01:16:56.905908Z","iopub.status.idle":"2022-08-06T01:16:57.014349Z","shell.execute_reply.started":"2022-08-06T01:16:56.905879Z","shell.execute_reply":"2022-08-06T01:16:57.013403Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ner_sent = sp(text[1])\n\nprint(f'Named Entity Recognition using SpaCy: \\n\\n{ner_sent} =>\\n')\n\nfor ent in ner_sent.ents:\n print(ent.text, '-', ent.label_)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:16:58.800754Z","iopub.execute_input":"2022-08-06T01:16:58.801073Z","iopub.status.idle":"2022-08-06T01:16:58.821180Z","shell.execute_reply.started":"2022-08-06T01:16:58.801043Z","shell.execute_reply":"2022-08-06T01:16:58.820484Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Named Entity Recognition Visualization using SpaCy')\ndisplacy.render(ner_sent, style = 'ent', jupyter = True)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:16:59.563880Z","iopub.execute_input":"2022-08-06T01:16:59.564198Z","iopub.status.idle":"2022-08-06T01:16:59.571978Z","shell.execute_reply.started":"2022-08-06T01:16:59.564169Z","shell.execute_reply":"2022-08-06T01:16:59.571077Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Word Embedding**\n\nWord embedding is the process of converting text into numerical data. Since computers work on only numbers and cannot accurately compute data in the form of strings, words are embedded into a unique integer or numerical code. For example, the word “Severe” is fixed as a number “3214”.\n\n![we1-min.png](attachment:we1-min.png)","metadata":{},"attachments":{"we1-min.png":{"image/png":"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"}}},{"cell_type":"code","source":"warnings.filterwarnings('ignore')\n\nword2vec = gensim.models.Word2Vec(abc.sents())\nembedding1 = word2vec.most_similar(tokens1[-1])\nvector1 = word2vec[tokens1[-1]]\n\nprint(f'Word Embeddibng using Word2Vec: \\n\\nMost Similar Words => {embedding1} \\n\\nWord Vector => {vector1}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:17:01.779753Z","iopub.execute_input":"2022-08-06T01:17:01.780083Z","iopub.status.idle":"2022-08-06T01:17:17.509884Z","shell.execute_reply.started":"2022-08-06T01:17:01.780051Z","shell.execute_reply":"2022-08-06T01:17:17.509025Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
\n 🤩 Visualizing Word Embedding with Word2Vec using PCA!
","metadata":{}},{"cell_type":"code","source":"pca = PCA(n_components = 2)\n\nX = word2vec[word2vec.wv.vocab]\nX = X[ : 10, : ]\n\nresult = pca.fit_transform(X)\nwords = list(word2vec.wv.vocab)\nwords = words[ : 10]\n\nplt.figure(figsize = (15, 20))\nplt.title('Word Embedding using PCA')\nplt.scatter(result[ : , 0], result[ : , 1])\nfor i, word in enumerate(words):\n plt.annotate(word, xy = (result[i, 0], result[i, 1]))\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:17:17.511624Z","iopub.execute_input":"2022-08-06T01:17:17.511957Z","iopub.status.idle":"2022-08-06T01:17:17.786743Z","shell.execute_reply.started":"2022-08-06T01:17:17.511921Z","shell.execute_reply":"2022-08-06T01:17:17.785910Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
🔦 For Word Embedding using GloVe, the embedding representation 'glove.6B.100d.txt' is used!
","metadata":{}},{"cell_type":"code","source":"input_file = '../input/glove6b/glove.6B.100d.txt'\noutput_file = '../glove.6B.100d.txt.word2vec'\n\nglove2word2vec(input_file, output_file)\n\nmodel = KeyedVectors.load_word2vec_format(output_file, binary = False)\nresult = model.most_similar(positive = ['king', 'woman'], negative = ['man'], topn = 1)\n\nprint(f'Word Embedding using GloVe: \\n{result}')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:17:23.700008Z","iopub.execute_input":"2022-08-06T01:17:23.700348Z","iopub.status.idle":"2022-08-06T01:18:05.601905Z","shell.execute_reply.started":"2022-08-06T01:17:23.700314Z","shell.execute_reply":"2022-08-06T01:18:05.601009Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Sentiment Analysis**\n\nSentimental analysis plays a significant role in determining the polarity of a review or a comment. It is used to know whether the person is talking about something in a positive way or a negative way. It can be classified broadly into positive, negative, and neutral. For example, on a tourism website, a person leaves a remark stating, “There are beautiful tourist spots in Switzerland. “The word ‘beautiful’ is positive as it describes Switzerland as pretty.\n\n![sentiment-analysis-min.png](attachment:sentiment-analysis-min.png)","metadata":{},"attachments":{"sentiment-analysis-min.png":{"image/png":"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"}}},{"cell_type":"code","source":"sia = SentimentIntensityAnalyzer()\npolarity_scores1 = sia.polarity_scores(text[10])\n\nprint(f\"Sentiment Analysis using NLTK: \\n{text[10]} => {polarity_scores1}\")","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:18:05.603819Z","iopub.execute_input":"2022-08-06T01:18:05.604207Z","iopub.status.idle":"2022-08-06T01:18:05.626075Z","shell.execute_reply.started":"2022-08-06T01:18:05.604165Z","shell.execute_reply":"2022-08-06T01:18:05.625441Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x = np.array([[sia.polarity_scores(word)['compound'] for word in word_tokenize(text[10])]])\nannot = np.array([word_tokenize(text[10])])\n\nplt.figure(figsize = (10, 3))\nsns.heatmap(x, annot = annot, fmt = '')\nplt.title('Heatmap for Sentiment Analysis')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:18:05.629221Z","iopub.execute_input":"2022-08-06T01:18:05.629496Z","iopub.status.idle":"2022-08-06T01:18:05.912612Z","shell.execute_reply.started":"2022-08-06T01:18:05.629472Z","shell.execute_reply":"2022-08-06T01:18:05.911255Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport re\nimport string\nimport os\n\nfrom wordcloud import WordCloud\n\n\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report, confusion_matrix, f1_score\n\nfrom bs4 import BeautifulSoup\n!pip install -q contractions\nimport contractions\nfrom textblob import TextBlob\nimport spacy","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:57:24.348385Z","iopub.execute_input":"2022-08-06T01:57:24.348707Z","iopub.status.idle":"2022-08-06T01:57:30.662148Z","shell.execute_reply.started":"2022-08-06T01:57:24.348678Z","shell.execute_reply":"2022-08-06T01:57:30.661089Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv(\"../input/nlp-getting-started/train.csv\")\ny=train.target.values\n\n# read test csv file as test ","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:42:30.369772Z","iopub.execute_input":"2022-08-06T01:42:30.370093Z","iopub.status.idle":"2022-08-06T01:42:30.400938Z","shell.execute_reply.started":"2022-08-06T01:42:30.370065Z","shell.execute_reply":"2022-08-06T01:42:30.400202Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Functions for clean data\n\nfrom spacy.lang.en.stop_words import STOP_WORDS as stopwords\nglobal stopwords\n\ndef get_words_counts(tweets):\n length = len(str(tweets).split())\n return length\n \ndef get_chars_counts(tweets):\n string = tweets.split()\n x = ''.join(string)\n return len(x)\n \ndef get_average_word_length(tweets):\n count = get_chars_counts(tweets)/get_words_counts(tweets)\n return count\n \ndef get_stopwords_count(tweets):\n global stopwords\n stopwords = len([t for t in tweets.split() if t in stopwords])\n return stopwords\n \ndef get_hashtags_tags(tweets):\n hashtags = len([t for t in tweets.split() if t.startswith('#')])\n return hashtags\n \ndef get_email_tags(tweets):\n email = len([t for t in tweets.split() if t.startwith('@')])\n return email\n \ndef get_digit_counts(tweets):\n digits = re.findall(r'[0-9,.]+', tweets)\n return digits\n \ndef get_uppercase_units(tweets):\n uppercase = len([t for t in tweets.split() if t.isupper()])\n return uppercase","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:51:59.185822Z","iopub.execute_input":"2022-08-06T01:51:59.186156Z","iopub.status.idle":"2022-08-06T01:51:59.196566Z","shell.execute_reply.started":"2022-08-06T01:51:59.186124Z","shell.execute_reply":"2022-08-06T01:51:59.195646Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# With this function we can get some features for build a bit EDA\n\ndef get_features(df):\n if type(df) == pd.core.frame.DataFrame:\n df['words_counts'] = df['text'].apply(lambda x: get_words_counts(x))\n df['char_counts'] = df['text'].apply(lambda x: get_chars_counts(x))\n df['average_word_length'] = df['text'].apply(lambda x: get_average_word_length(x))\n# df['stopwords_counts'] = df['text'].apply(lambda x: get_stopwords_count(x))\n df['hashtags_counts'] = df['text'].apply(lambda x: get_hashtags_tags(x))\n# df['email_counts'] = df['text'].apply(lambda x: get_email_tags(x))\n df['digits_counts'] = df['text'].apply(lambda x: get_digit_counts(x))\n df['uppercase_counts'] = df['text'].apply(lambda x: get_uppercase_units(x))\n \n else:\n print('ERROR')\n \n return df","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:52:09.284908Z","iopub.execute_input":"2022-08-06T01:52:09.285239Z","iopub.status.idle":"2022-08-06T01:52:09.294537Z","shell.execute_reply.started":"2022-08-06T01:52:09.285208Z","shell.execute_reply":"2022-08-06T01:52:09.291623Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = get_features(train)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:52:23.374732Z","iopub.execute_input":"2022-08-06T01:52:23.375053Z","iopub.status.idle":"2022-08-06T01:52:23.503125Z","shell.execute_reply.started":"2022-08-06T01:52:23.375022Z","shell.execute_reply":"2022-08-06T01:52:23.502439Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def remove_tweet_username(df):\n return re.sub('@[^\\s]+','', df)\n\ndef make_lower(df):\n return df.lower()\n\ndef cont_exp(df):\n return contractions.fix(df)\n\ndef make_string(df):\n return str(df)\n\ndef remove_url(df):\n return re.sub(r'(http|https|ftp|ssh)://([\\w_-]+(?:(?:\\.[\\w_-]+)+))([\\w.,@?^=%&:/~+#-]*[\\w@?^=%&/~+#-])?', '' , df)\n\ndef remove_email(df):\n return re.sub(r'([a-z0-9+._-]+@[a-z0-9+._-]+\\.[a-z0-9+_-]+)',\"\", df)\n\n#Retweets\ndef remove_rt(df):\n df = str(df)\n return re.sub(r'\\brt\\b', \"\", df).strip()\n\ndef remove_html(df):\n return BeautifulSoup(df, 'lxml').get_text().strip()\n\n\ndef remove_dots(df):\n dot_pattern = re.compile(r'\\.{1,}')\n single_dot = dot_pattern.sub(' ', df)\n return single_dot\n\ndef remove_special_chars(df):\n df = re.sub(r'[^\\w]+', \" \", df)\n df = ' '.join(df.split())\n \n return df\n\ndef make_base(df):\n df = str(df)\n x_list = []\n doc = nlp(df)\n\n for token in doc:\n lemma = token.lemma_\n if lemma == '-PRON-' or lemma == 'be':\n lemma = token.text\n\n x_list.append(lemma)\n return ' '.join(x_list)\n\ndef spelling_correction(df):\n df = TextBlob(df).correct()\n return df\n\ndef resub(df):\n return re.sub(\"(.)\\\\1{2,}\", \"\\\\1\", df)\n\n\n\ndef get_clean_data(df):\n df = remove_url(df)\n df = remove_email(df)\n# df = remove_special_chars(df)\n df = remove_html(df)\n# df = remove_dots(df)\n# df = make_base(df)\n# df = spelling_correction(df).raw_sentences[0]\n df = make_lower(df)\n df = make_string(df)\n df = cont_exp(df)\n df = remove_rt(df)\n df = resub(df) \n df = remove_tweet_username(df)\n \n return df","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:52:33.743263Z","iopub.execute_input":"2022-08-06T01:52:33.743621Z","iopub.status.idle":"2022-08-06T01:52:33.765530Z","shell.execute_reply.started":"2022-08-06T01:52:33.743590Z","shell.execute_reply":"2022-08-06T01:52:33.764471Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train['text'] = train['text'].apply(get_clean_data)\ntest['text'] = test['text'].apply(get_clean_data)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:53:33.600500Z","iopub.execute_input":"2022-08-06T01:53:33.600849Z","iopub.status.idle":"2022-08-06T01:53:36.285568Z","shell.execute_reply.started":"2022-08-06T01:53:33.600809Z","shell.execute_reply":"2022-08-06T01:53:36.284749Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train['text'] = train['text'].apply(remove_special_chars)\ntest['text'] = test['text'].apply(remove_special_chars)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:53:48.892914Z","iopub.execute_input":"2022-08-06T01:53:48.893256Z","iopub.status.idle":"2022-08-06T01:53:48.982121Z","shell.execute_reply.started":"2022-08-06T01:53:48.893225Z","shell.execute_reply":"2022-08-06T01:53:48.981384Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from gensim.parsing.preprocessing import STOPWORDS","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:54:06.552569Z","iopub.execute_input":"2022-08-06T01:54:06.552898Z","iopub.status.idle":"2022-08-06T01:54:06.557741Z","shell.execute_reply.started":"2022-08-06T01:54:06.552866Z","shell.execute_reply":"2022-08-06T01:54:06.556331Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"STOPWORDS.difference()\n\nall_stopwords_gensim = STOPWORDS\nsw_list = {\"not\"}\nall_stopwords_gensim = STOPWORDS.difference(sw_list)\n\n# Use Gensim\n\nfrom gensim.parsing.preprocessing import remove_stopwords\n\ntrain['text'] = train['text'].apply(remove_stopwords)\ntest['text'] = test['text'].apply(remove_stopwords)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:54:19.767487Z","iopub.execute_input":"2022-08-06T01:54:19.767816Z","iopub.status.idle":"2022-08-06T01:54:19.815433Z","shell.execute_reply.started":"2022-08-06T01:54:19.767786Z","shell.execute_reply":"2022-08-06T01:54:19.814724Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# trying to remove all digits from a columns\n\ntrain['text'] = train['text'].str.replace('\\d+', '')\ntest['text'] = test['text'].str.replace('\\d+', '')","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:55:02.359744Z","iopub.execute_input":"2022-08-06T01:55:02.360082Z","iopub.status.idle":"2022-08-06T01:55:02.392874Z","shell.execute_reply.started":"2022-08-06T01:55:02.360051Z","shell.execute_reply":"2022-08-06T01:55:02.392181Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"text = train['text']\ny = train['target']\n\ntfidf = TfidfVectorizer()\nX = tfidf.fit_transform(text)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state = 7, stratify=y)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:55:29.655078Z","iopub.execute_input":"2022-08-06T01:55:29.655416Z","iopub.status.idle":"2022-08-06T01:55:29.850950Z","shell.execute_reply.started":"2022-08-06T01:55:29.655384Z","shell.execute_reply":"2022-08-06T01:55:29.850016Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nclf_lr = LogisticRegression()\n\ndef run_LR(clf_lr, X_train, X_test, y_train, y_test):\n \n clf_lr.fit(X_train, y_train)\n y_pred = clf_lr.predict(X_test)\n \n print()\n print('Classification Report:')\n print(classification_report(y_test, y_pred))\n print(f\"f1_score: {f1_score(y_test, y_pred)}\")\n \nrun_LR(clf_lr, X_train, X_test, y_train, y_test)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T01:57:51.675993Z","iopub.execute_input":"2022-08-06T01:57:51.676362Z","iopub.status.idle":"2022-08-06T01:57:51.833530Z","shell.execute_reply.started":"2022-08-06T01:57:51.676324Z","shell.execute_reply":"2022-08-06T01:57:51.832272Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import en_core_web_lg\nnlp = en_core_web_lg.load()\n\ndef get_vec(x):\n doc = nlp(x)\n vec = doc.vector\n return vec","metadata":{"execution":{"iopub.status.busy":"2022-08-06T02:02:41.525836Z","iopub.execute_input":"2022-08-06T02:02:41.526203Z","iopub.status.idle":"2022-08-06T02:02:43.593267Z","shell.execute_reply.started":"2022-08-06T02:02:41.526162Z","shell.execute_reply":"2022-08-06T02:02:43.592370Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train['vectors'] = train['text'].apply(lambda x: get_vec(x))\nX = train['vectors'].to_numpy()\nX = X.reshape(-1, 1)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T02:03:14.867051Z","iopub.execute_input":"2022-08-06T02:03:14.867391Z","iopub.status.idle":"2022-08-06T02:03:58.759758Z","shell.execute_reply.started":"2022-08-06T02:03:14.867359Z","shell.execute_reply":"2022-08-06T02:03:58.758986Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"### Normalization\n\nfrom sklearn.preprocessing import normalize\nX = np.concatenate(np.concatenate(X, axis = 0), axis = 0).reshape(-1, 300)\nX = normalize(X)\ny = train['target']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state = 7, stratify=y)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T02:04:20.974794Z","iopub.execute_input":"2022-08-06T02:04:20.975159Z","iopub.status.idle":"2022-08-06T02:04:21.006893Z","shell.execute_reply.started":"2022-08-06T02:04:20.975129Z","shell.execute_reply":"2022-08-06T02:04:21.006122Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nclf_lr = LogisticRegression()\n\ndef run_LR(clf_lr, X_train, X_test, y_train, y_test):\n \n clf_lr.fit(X_train, y_train)\n y_pred = clf_lr.predict(X_test)\n \n print()\n print('Classification Report:')\n print(classification_report(y_test, y_pred))\n print(f\"f1_score: {f1_score(y_test, y_pred)}\")\n \nrun_LR(clf_lr, X_train, X_test, y_train, y_test)","metadata":{"execution":{"iopub.status.busy":"2022-08-06T02:04:39.226726Z","iopub.execute_input":"2022-08-06T02:04:39.227083Z","iopub.status.idle":"2022-08-06T02:04:39.331940Z","shell.execute_reply.started":"2022-08-06T02:04:39.227053Z","shell.execute_reply":"2022-08-06T02:04:39.330990Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}