Naïve Bayes Algorithm For Sentiment Analysis Windows Phone Store Application Reviews

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Normah Normah

Abstract

Reading reviews helps consumers choose the applications, helping companies and developers monitor user satisfaction to improve quality of features and services, read overall and manually could spend the time and laborious, if read at a glance, information not conveyed perfectly. This study analyzes user sentiment Windows Phone Store applications by automatically classifying reviews into positive or negative opinion category. Naïve bayes has good potential because of its simplicity and performance as a model of classifying text on many domains. The model was evaluated using 10 Fold Cross Validation. Measurements were made with the Confusion Matrix and the ROC curve. The accuracy produced in this study is 84.50%, indicating that Naïve Bayes is a good model in classifying text especially in the case of sentiment analysis.

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NORMAH, Normah. Naïve Bayes Algorithm For Sentiment Analysis Windows Phone Store Application Reviews. SinkrOn, [S.l.], v. 3, n. 2, p. 13-19, mar. 2019. ISSN 2541-2019. Available at: <http://jurnal.polgan.ac.id/index.php/sinkron/article/view/242>. Date accessed: 21 july 2019. doi: https://doi.org/10.33395/sinkron.v3i2.242.
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