Mastering-Sentiment-Analysis

This notebook contains the most promised techniques that are implemented to classify texts ex. reviews.

Notebook Contents:

• Frame the problem and look at the big picture:
• Get The Data
• Model-1 Logistic + BOW
• Model 2 NaiveBayes + BOW
• Model-3 Naive Bayes with TFIDF
• Model-4 Naive Bayes with Ngrams
• Text processing
• Model 5 cleaned text Naive Bayes BOW and
• Model 6 Fastext vector with Naive Bayes Baseline
• Model 7 - Glove vectors with Baseline model
• Model Hyperparameter tuning best models
• Model- 8 LSTM using glove word2vec
• Model 9 LSTM word2vec + early stopping
• Model 10 bidirectional LSTM
• Model 11 GRU 2 layers
• Model 12 LSTM multiple layers
• Classify text with HuggingFace pipelines
• Classify text with BERT
• Classify text with RoBERTa
• Classify text with GPT