Multinomial Naïve Bayes Classifier for Sentiment Analysis of Internet Movie Database

Abstract

Sentiment analysis (SA), also known as opinion mining, is a natural language processing (NLP) technique used to determine the sentiment or emotional tone behind a piece of text. It involves analyzing the text to identify whether it expresses a positive, negative, or neutral sentiment. SA can be applied to various types of text data such as social media posts, customer reviews, news articles, and more. This experiment is based on the Internet Movie Database (IMDB) dataset, which comprises movie reviews and the positive or negative labels related to them. Our research experiment’s objective is to identify the model with the best accuracy and the most generality. Text preprocessing is the first and most critical phase in an NLP system since it significantly impacts the overall accuracy of the classification algorithms. The experiment implements unsupervised sentiment classification algorithms including Valence Aware Dictionary and sentiment Reasoner (VADER) and TextBlob. We also examine the supervised sentiment classifications methods such as Naïve Bayes (Bernoulli NB and Multinomial NB). The Term Frequency-Inverse Document Frequency (TFIDF) model is used to feature selection and extractions. The combination of Multinomial NB and TFIDF achieves the highest accuracy, 87.63%, for both classification reports based on our experiment result.

Keywords

• Sentiment analysis
• Naïve Bayes
• Bernoulli NB
• multinomial NB
• TFIDF
• sentiment classifications