Optimization of Naïve Bayes Classifier By Implemented Unigram, Bigram, Trigram for Sentiment Analysis of Hotel Review

Abstract

The information needed in its development requires that proper analysis can provide support in making decisions. Sentiment analysis is a data processing technique that can be completed properly. To make it easy to classify hotels based on sentiment analysis using the Naїve Bayes Classifier algorithm. As a classification tool, Naїve Bayes Classifier is considered efficient and simple. In this study consists of 3 stages of sentiment analysis process. The first stage is text pre-processing which consists of transform case, stopword removal, and stemming. The second stage is the implementation of N-Gram features, namely Unigram, Bigram, Trigram. The N-Gram feature is a feature that contains a collection of words that will be referred to in the next process. Next, the last click is the hotel review classification process using Na menggunakanve Bayes Classifier. OpinRank Hotels Review dataset on Naїve Bayes Classifier using N-Gram namely Unigram, Bigram, Trigram with research results that show Unigram can provide better test results than Bigram and Trigram with an average accuracy of 81.30%.