Sentiment Analysis customer Towards Cinema Services in Semarang Using Naive Bayes Classifier on Google Reviews

Abstract

The development of the entertainment industry, especially in the field of cinema, encourages every service provider to continuously maintain the quality of their services. One method of assessing customer satisfaction is through sentiment testing. The main objective of this study is to examine customer sentiment towards cinema services in Semarang by applying the Naive Bayes Classifier method. The research data was taken from 600 customer reviews on Google Review, which were then divided into two groups: training data consisting of 480 reviews (80%) and testing data consisting of 120 reviews (20%). Before the classification process, the data underwent pre-processing stages involving data cleaning, case folding, tokenization, stopword removal, and stemming, followed by data labeling into two sentiment categories, namely positive and negative. This study took five cinemas as objects, namely CitraXXI, Cinépolis Java Mall, Paragon XXI, XXI Uptown Mall, and XXI DP Mall. The classification results show that the Naive Bayes algorithm is able to group sentiments quite well, with model accuracy ranging from 0.90 to 0.94. Of the five cinemas, Cinépolis Java Mall achieved the highest accuracy, which was 0.94.The development of the entertainment industry, especially in the field of cinema, encourages every service provider to continuously maintain the quality of their services. One method of assessing customer satisfaction is through sentiment testing. The main objective of this study is to examine customer sentiment towards cinema services in Semarang by applying the Naive Bayes Classifier method. The research data was taken from 600 customer reviews on Google Review, which were then divided into two groups: training data consisting of 480 reviews (80%) and testing data consisting of 120 reviews (20%). Before the classification process, the data underwent pre-processing stages involving data cleaning, case folding, tokenization, stopword removal, and stemming, followed by data labeling into two sentiment categories, namely positive and negative. This study took five cinemas as objects, namely CitraXXI, Cinépolis Java Mall, Paragon XXI, XXI Uptown Mall, and XXI DP Mall. The classification results show that the Naive Bayes algorithm is able to group sentiments quite well, with model accuracy ranging from 0.90 to 0.94. Of the five cinemas, Cinépolis Java Mall achieved the highest accuracy, which was 0.94.