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Opinion mining is another term for sentiment analysis. It requires analyzing textual data to determine the text's underlying emotional tone or attitude. The main objective of sentiment analysis is to categorize content as positive or negative. This classification offers useful perceptions of the tone of a text, such as product reviews or film criticism. The major goal of this particular paper is to use several machine-learning approaches to improve sentiment analysis. A valuable method for predicting the tone of movie reviews and determining how people feel about particular goods or services is the incorporation of machine learning into sentiment analysis. This paper carefully examines a variety of machine learning algorithms, including RNN, Bi-LSTM, and CNN using Global Average Pooling (GAP). The study thoroughly evaluates the degrees of accuracy, benefits, and restrictions of each approach. Remarkably, the study shows that the Bi-LSTM model when used with pre-trained word2vec achieved a higher accuracy of 95.35%
G et al. (Thu,) studied this question.