Multi Aspect Sentiment of Beauty Product Reviews using SVM and Semantic Similarity

  • irbah salsabila Telkom University
  • Yuliant Sibaroni Telkom University
Keywords: Support Vector Machine, Semantic Similarity, TF-IDF

Abstract

Beauty products are an important requirement for people, especially women. But, not all beauty products give the expected results. A review in the form of opinion can help the consumers to know the overview of the product. The reviews were analyzed using a multi-aspect-based approach to determine the aspects of the beauty category based on the reviews written on femaledaily.com. First, the review goes through the preprocessing stage to make it easier to be processed, and then it used the Support Vector Machine (SVM) method with the addition of Semantic Similarity and TF-IDF weighting. From the test result using semantic, get an accuracy of 93% on the price aspect, 92% on the packaging aspect, and 86% on the scent aspect.

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Published
2021-06-19
How to Cite
irbah salsabila, & Yuliant Sibaroni. (2021). Multi Aspect Sentiment of Beauty Product Reviews using SVM and Semantic Similarity. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(3), 520 - 526. https://doi.org/10.29207/resti.v5i3.3078
Section
Artikel Teknologi Informasi