Machine Learning-Based Sentiment Analysis of Movie Review

Abstract

An important method for evaluating a film’s performance is through movie reviews. A collection of movie reviews is what provides us with a deeper qualitative insight on various aspects of the movie, whereas providing a movie with a numerical rating in the form of stars tells us about the success or failure of the movie quantitatively. We can learn about the movie’s strengths and weaknesses from a textual review, and a morein-depth analysis of a movie review can tell us if the movie overall meets the reviewer’s expectations. One of the most important areas of machine learning is sentiment analysis, which seeks to extract subjective information from written reviews. Natural language processing and text mining are closely related to sentiment analysis. It can be used to determine the reviewer’s perspective on a variety of subjects or the review’s overall polarity. Using sentiment analysis, we can determine whether the reviewer was ”positive,” ”negative,” and so on while providing their feedback. In this project, we want to use Sentiment Analysis on a set of movie reviews written by reviewers to figure out how they felt about the movie overall, such as whether they liked it or hated it. We want to use the relationships between the words in the review to predict the review’s overall polarity.