Analyzing Sentiment for Opinion Mining of Large Movie Reviews Using Naive Bayes with Word Frequency

The sentiment mining field (also known as opinion mining, opinion extraction, sentiment analysis (SA), sentiment extraction, and so on) has seen significant growth in academia. Researchers have experimented with a variety of approaches to automate SA and other fields in the fields of machine learning (ML), data mining, and natural language processing. Our research aims to develop a model that extracts and categorizes words from a specific text. In this study, we used TF-IDF to select 500 to 20,000 words with vector. After pre-processing and frequency-dependent word extraction, constructs are generated. Four Naive Bayes models (complement, multinomial, Bernoulli, and Gaussian) were used. The kappa scale, precision and accuracy scores, and F1 score were used to evaluate the proposed model. The Naive Bayes multinomial system produced the most accurate results, with an accuracy rate of 86.46 percent, according to the findings.