Enhancing machine-learning methods for sentiment classification of web data

Publication Type

Conference Proceeding Article

Publication Date

12-2014

Abstract

With advances in Web technologies, more and more people are turning to popular social media platforms such as Twitter to express their feelings and opinions on a variety of topics and current issues online. Sentiment analysis of Web data is becoming a fast and effective way of evaluating public opinion and sentiment for use in marketing and social behavioral studies. This research investigates the enhancement techniques in machine-learning methods for sentiment classification of Web data. Feature selection, negation dealing, and emoticon handling are studied in this paper for their ability to improve the performance of machine-learning methods. The range of enhancement techniques is tested using different text data sets, such as tweets and movie reviews. The results show that different enhancement methods can improve classification efficacy and accuracy differently.

Keywords

Web data, Emoticon handling, Feature selection, Hybrid method, Machine learning, Negation dealing, Sentiment classification, Twitter

Discipline

Numerical Analysis and Scientific Computing | Social Media

Research Areas

Intelligent Systems and Optimization

Publication

Information Retrieval Technology: 10th Asia Information Retrieval Societies Conference, AIRS 2014, Kuching, Malaysia, December 3-5: Proceedings

Volume

8870

First Page

394

Last Page

405

ISBN

9783319128443

Identifier

10.1007/978-3-319-12844-3_34

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-3-319-12844-3_34

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