Multi-aspect sentiment analysis on netflix application using latent dirichlet allocation and support vector machine methods

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Attala Rafid Abelard
Yuliant Sibaroni


Among many film streaming platforms that have sprung up, Netflix is ​​the platform that has the most subscribers compared to the other platforms. However, not all reviews provided by the Netflix users are good reviews. These reviews will later be analyzed to determine what aspects are reviewed by the users based on reviews written on the Google Play Store, using the Latent Dirichlet Allocation (LDA) method. Then, the classification process using the Support Vector Machine (SVM) method will be carried out to determine whether each of these reviews is included in the positive or negative class (Sentiment Analysis). There are 2 scenarios that were carried out in this study. The first scenario resulted that the best number of LDA topics to be used is 40, and the second scenario resulted that the use of filtering process in the preprocessing stage reduces the score of the f1-score. Thus, this study resulted in the best performance score on LDA and SVM testing with 40 topics, and without running the filtering process with the score of 78.15%.


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How to Cite
A. Abelard and Y. Sibaroni, “Multi-aspect sentiment analysis on netflix application using latent dirichlet allocation and support vector machine methods”, INFOTEL, vol. 13, no. 3, pp. 128-133, Aug. 2021.


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