A Study on Improving Sentiment Classification Performance of Textual Data

• Main Authors: Chia-wei Chang, 張家偉
• Other Authors: Long-sheng Chen
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/32586251145999792149

碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 99 === With rapid development of the text based communication platforms, such as Blogs, Microblog, Twiter, and so on, the opinions and comments of products/services expressed by users can spread quickly in the cyber space, and affect other consumer’s purchase intentions or brand impressions. Facing with promptly increasing reviews on the Web, how to effectively detect user’s sentiment, especially negative sentiment, has become one of emerging research issues. In order to tackle this task and improve sentiment classification performance, this study aims to propose two feature selection methods called “Modified categorical proportional difference, MCPD” and “Feature category orientated, FCO” approach for dimension reduction. In addition, several actual cases of online users’ comments will be used to illustrate the effectiveness of our proposed methods. And support vector machines (SVM) have been employed to construct classifiers. Experimental results indicated that our proposed MCPD method has the better classification performance while using fewer features, and FCO approach can improve sentiment classification performance of textual data.