Sentimental Analysis of Movie Reviews Using Machine Learning

Abstract

Sentiment analysis is a rapidly growing field in natural language processing that aims to extract subjective information from text data. One of the most common applications of sentiment analysis is in the movie industry, where it is used to gauge public opinion on films. In this research paper, a sentimental analysis of movie reviews has been presented using a dataset of over 25,000 reviews collected from various sources. A machine learning model with different classifiers was built using Naïve Bayes, Logistic Regression and Support Vector Machines for classifying movie reviews as positive, negative or neutral. A comparison of three popular machine learning algorithms was made. After pre-processing the dataset by removing stop words, a stemming technique was applied to reduce the dimensionality of the dataset. The recognition algorithms were evaluated in terms of performance matrices such as accuracy, precision, recall and F1-score. Compared to others, it was observed that the SVM algorithm performed the best among all three algorithms, achieving an accuracy of 73%. The results of this analysis demonstrated the effectiveness of the model in accurately classifying movie reviews and provided valuable insights into the current state of public opinion on films. The comparison of the three algorithms provided insight into the best algorithm to be used for a specific dataset and scenario.