| Summary: | Millions of messages and thousands of articles are posted every day, and this information is stored
in an unstructured natural text. Natural Language Processing (NLP) is a study to understand text
using computational techniques. One of the most important tasks in NLP is sentiment analysis
which studies people’s opinions, emotions, and attitudes. Sentiment analysis is a challenging task
involving context understanding, language use, and unstructured human text.
This project aims to use sentiment analysis techniques using different deep learning techniques. It
will focus on binary sentiment classification, which detects the polarity in a text into 2 classes,
positive and negative.
This project studied different sentiment analysis techniques such as VADER,SVM, Naïve Bayes
CNN,RNN, LSTM, GRU, and BERT. BERT gives the best accuracy among the available
techniques but with the drawback that it takes a longer time to train.
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