Classification of movie reviews using term frequency-inverse document frequency and optimized machine learning algorithms.

ABSTRACT: The Internet Movie Database (IMDb), being one of the popular online databases for movies and personalities, provides a wide range of movie reviews from millions of users. This provides a diverse and large dataset to analyze users' sentiments about various personalities and movies. Despite being helpful to provide the critique of movies, the reviews on IMDb cannot be read as a whole and requires automated tools to provide insights on the sentiments in such reviews. This study provides the implementation of various machine learning models to measure the polarity of the sentiments presented in user reviews on the IMDb website. For this purpose, the reviews are first preprocessed to remove redundant information and noise, and then various classification models like support vector machines (SVM

SUBMITTER: Naeem MZ 

PROVIDER: S-EPMC9044332 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature