Text Mining Untuk Analisis Sentimen Review Film Menggunakan Algoritma Naïve Bayes

Abstract

In its development, information technology brought about many changes and advances in the context of everyday life. One of the benefits of information technology to process large amounts of data is text mining. A movie is a spectacle that can be done at a relaxed time. Currently, there are many movies that can be watched via the internet or cinema. Movies that are watched on the internet are sometimes charged to watch so that potential viewers before watching a movie will read comments from users who have watched the movie. Film business and its individual reviews cannot be separated and film review sites such as IMDb is a credible source of reviews posted in public forums. Movie comments are many and varied on the IMDB website, we can see comments based on the movie title. This causes users to have difficulty analyzing other users' comments. So, this study aims to analyze the sentiment of opinions from several comments from IMDB website users and will be classified using the naïve bayes classifier method. Sentiment analysis is a classification process to understand the opinions, interactions, and emotions of a document or text. Naïve Bayes is suitable for solving multi-class prediction problems. If its assumption of the independence of features holds true, it can perform better than other models and requires much less training data. In addition to the Naïve Bayes Classifier, TF-IDF technique is also used to change the shape of the document into several words. The results obtained by applying the Naïve Bayes method are 87,2% accuracy, 92,4% recall, 80,9% precision, and 12,8% error rate.

Keywords : Classification, IMDb, Naïve Bayes, Sentiment Analysis