Enhancing machine-learning methods for sentiment classification of web data

• Main Authors: WANG, Zhaoxia, TONG, Victor Joo Chuan, CHIN, Hoong Chor
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Web data; Emoticon handling; Feature selection; Hybrid method; Machine learning; Negation dealing; Sentiment classification; Twitter; Numerical Analysis and Scientific Computing; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/5642
• Institution: Singapore Management University

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.