An Experimental Study of Supervised Sentiment Analysis Using Gaussian Naïve Bayes

• Published in: 2018 International Seminar on Application for Technology of Information and Communication pp. 476 - 481
• Main Authors: Widodo Wijayanto, Unggul, Sarno, Riyanarto
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2018
• Subjects: cross-validation; data training; Feature extraction; features selection; Machine learning; Mathematical model; Motion pictures; Seminars; Sentiment analysis; stopwords; supervised; text mining; Training
• DOI: 10.1109/ISEMANTIC.2018.8549788

In this millennial generation everyone using technology at higher rates than people from other generations. It means the millennial generation is aware of evolving technology. Many companies are taking chances by receiving customer reviews through applications. In this study, we use customer reviews from Yelp (foods), IMDb (movies) and Amazon (products). The reviews received by the company are numerous. Product management does not have much time to read customer reviews one by one. So, to speed up the reading of customer reviews we were using sentiment analysis. There are many methods that used in sentiment analysis such as supervised sentiment analysis. We used TF-IDF to convert word to features implements the supervised method. Performance of the supervised method depends on the data training quality. So, to improve the accuracy of the results by improving data training quality. The methods used to improve the data training quality in this paper using CHI2 Features Selection and Stopwords. In this study, we use K-folds Cross-validation to get valid results. This study proves the use of Context-based Stopwords can improve the results. Context-based Stopwords enrich the number of Stopwords that removing bias features.