Sentiment Classification for IMDB Movie Reviews in Benchmark Dataset Using LR, MNB and SGD

Abstract

A breakdown of all movie reviews can help viewers decide by saving them the time it would take to read all of the reviews. Critics commonly use movie-rating websites to submit comments and rate films, so guiding viewers in determining whether or not to see the film. Sentiment analysis was used to determine reviewers' attitudes based on their opinions. Sentiment analysis of a movie review can help assess how positive or negative a review is, and thus the film's overall rating. In this research, the sentiment classification methods LR, MNB, and SGD are suggested for a big movie review data set. We chose 50K IMDB movie review entries that are entirely written in English, with 25K positive and 25K negative ratings. The results show that the Multinomial Naive Bayes (MNB) algorithm outperforms other classification algorithms in terms of mistake rate and accuracy.