IJAIEM

Pushpa Soumya, S. Jyothsna

Abstract

Finding the polarity (sentiment) and intention of a piece of textual data is the core emphasis of sentiment analysis, one of the key challenges in natural language processing. Data may be presented at various levels, from sentences to whole documents. It is much simpler to convey our feelings when we speak to one another as humans. However, it gets more difficult to discern the emotions when dealing with robots. Text analytics and other natural language processing approaches are used in sentiment analysis. Critical reception is a major factor in determining a film's box office success or failure. A wider audience will be greatly impacted by these evaluations. As a result, creating a reliable model that can correctly categorize movie reviews is crucial. This research uses six different machine learning models to categorize movie reviews, and then compares and contrasts them to find the top classifier according to a number of criteria.