Aspect-based Sentiment Analysis to Review Products Using Naive Bayes

被引：52

作者：

Mubarok, Mohamad Syahrul ^{[1
]}

Adiwijaya ^{[1
]}

Aldhi, Muhammad Dwi ^{[1
]}

机构：

[1] Telkom Univ, Sch Comp, Jl Telekomunikasi 1 Terusan Buah Batu, Bandung 40257, Indonesia

来源：

INTERNATIONAL CONFERENCE ON MATHEMATICS: PURE, APPLIED AND COMPUTATION: EMPOWERING ENGINEERING USING MATHEMATICS | 2017年 / 1867卷

关键词：

Sentiment Analysis; Preprocessing; POS Tagging; Chi Square; Naive Bayes;

D O I：

10.1063/1.4994463

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Product reviews can provide great benefits for consumers and producers. Number of reviews could be ranging from hundreds to thousands and containing various opinions. These make the process of analyzing and extracting information on existing reviews become increasingly difficult. In this research, sentiment analysis was used to analyze and extract sentiment polarity on product reviews based on a specific aspect of the product. This research was conducted in three phases, such as data preprocessing which involves part-of-speech (POS) tagging, feature selection using Chi Square, and classification of sentiment polarity of aspects using Naive Bayes. Based on evaluation results, it is known that the system is able to perform aspect-based sentiment analysis with its highest F1-Measure of 78.12%.

引用

页数：8

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