Comparison of Feature Selection Methods for Sentiment Analysis

被引：9

作者：

El Mrabti, Soufiane ^{[1
]}

Al Achhab, Mohammed ^{[1
]}

Lazaar, Mohamed ^{[1
]}

机构：

[1] Abdelmalek Essaadi Univ, ENSA, Tetouan, Morocco

来源：

BIG DATA, CLOUD AND APPLICATIONS, BDCA 2018 | 2018年 / 872卷

关键词：

Sentiment analysis; Feature selection; Text classification; Natural language processing; ALGORITHMS;

D O I：

10.1007/978-3-319-96292-4_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Sentiment analysis is process of deriving the opinion or attitude expressed in input text. For the classification problem, feature selection aims to select features that are capable of discriminating samples that belong to different classes. This paper evaluates the performance of three feature selection methods (MI, CHI and ANOVA) combined with three machine learning based classification techniques (NB, SVM and KNN) for sentiment analysis on online movie reviews dataset. The paper shows that feature selection is important task for sentiment based classification.

引用

页码：261 / 272

页数：12

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