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
Citation
1
Additional URL
https://doi.org/10.1007/978-3-319-12844-3_34