Moozonian
Web Images Developer News Books Maps Shopping Moo-AI
Showing results for elon Vector
GitHub Repo https://github.com/vaitybharati/Assignment-11-Text-Mining-01-Elon-Musk

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.
GitHub Repo https://github.com/ajay3850/-Assignment-11-Text-Mining-Elon-Musk

ajay3850/-Assignment-11-Text-Mining-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 Punctuations Remove https or url within text Converting into Text Tokens Tokenization Remove Stopwords Normalize the data Stemming (Optional) Lemmatization Feature Extaction Using BoW CountVectorizer CountVectorizer with N-grams (Bigrams & Trigrams) TF-IDF Vectorizer Generate Word Cloud Named Entity Recognition (NER) Emotion Mining - Sentiment Analysis
GitHub Repo https://github.com/rheaj30/Sentiment_analysis

rheaj30/Sentiment_analysis

Conducted sentiment analysis on 1 million Elon Musk tweets utilizing embedding learning with three models: Support Vector Machine, Random Forest, and Naive Bayes. Generated a labeled dataset categorizing tweets into positive, negative, and neutral sentiments.