GitHub / vaitybharati / Assignment-11-Text-Mining-01-Elon-Musk

Assignment-11-Text-Mining-01-Elon-Musk, Perform sentimental analysis on the Elon-musk tweets (Exlon-musk.csv), Text Preprocessing: remove both the leading and the trailing characters, removes empty strings, because they are considered in Python as False, Joining the list into one string/text, Remove Twitter username handles from a given twitter text. (Removes @usernames), Again Joining the list into one string/text, Remove Punctuation, Remove https or url within text, Converting into Text Tokens, Tokenization, Remove Stopwords, Normalize the data, Stemming (Optional), Lemmatization, Feature Extraction, Using BoW CountVectorizer, CountVectorizer with N-grams (Bigrams & Trigrams), TF-IDF Vectorizer, Generate Word Cloud, Named Entity Recognition (NER), Emotion Mining - Sentiment Analysis.

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Topics: affinity-scores, clean-tweets, correlation-analysis, countvectorizer, emotion-lexicon, emotion-mining, feature-extraction, lemmatization, n-grams, named-entity-recognition, pos-tagging, sentiment-analysis, sentiment-score, sentiment-value, text-mining, text-preprocessing, tfidf-vectorizer, tokenization, tweets-dataset, word-cloud

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