A Novel Feature-Based Text Classification Improving the Accuracy of Twitter Sentiment Analysis

被引：1

作者：

Wang, Yili ^{[1
]}

Sun, Le ^{[2
]}

Wang, Jin ^{[3
]}

Zheng, Yuhui ^{[2
]}

Youn, Hee Yong ^{[4
]}

机构：

[1] Sungkyunkwan Univ, Coll Informat & Commun Engn, Suwon, South Korea

[2] NUIST, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China

[3] Yangzhou Univ, Coll Informat Engn, Yangzhou 215127, Jiangsu, Peoples R China

[4] Sungkyunkwan Univ, Coll Software, Suwon 440746, South Korea

来源：

ADVANCES IN COMPUTER SCIENCE AND UBIQUITOUS COMPUTING | 2018年 / 474卷

基金：

新加坡国家研究基金会;

关键词：

Sentiment analysis; Text classification; Part of speech;

D O I：

10.1007/978-981-10-7605-3_72

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the growth of Internet and various online services, tremendous amount of data are generated in real time. As a result, sentiment analysis of online reviews has become an important research problem. In this paper a novel feature selection and weighting scheme is proposed for the sentiment analysis of twitter data. The Part of Speech (POS) tagging and Bayes-based Classifier are utilized in the proposed scheme. Also, different from the existing schemes, independency of the attributes and the influence of emotional words are properly manipulated in deciding the polarity of test data. Computer simulation with Sentiment 140 workload shows that the proposed scheme significantly outperforms the existing sentiment analysis schemes such as naive Bayes classifier and selective Bayes classifier.

引用

页码：440 / 445

页数：6

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