doi: 10.4304/jsw.6.8.1417-1428
Orientation Mining-Driven Approach to Analyze Web Public Sentiment
- 1 School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
- 2 College of Public Administration, Huazhong University of Science and Technology, Wuhan, China
- 3 Department of Mathematics, Nanjing University, Nanjin, China
Abstract
In recent years, Internet provides a unique opportunity to express and spread public sentiment, which makes the web contents becoming the largest information source of public sentiment. Since web public sentiment reflects people’s attitude to society and politics, the public opinion’s orientation is significant to decision-makers. In this paper, we utilize VSM (vector space model) to present the text orientation of web information and offer data-mining approaches to analyze public opinion’s orientation, which can assist decision-makers to steer social information and guide the web public sentiment. To achieve the goal of text orientation analysis, two ways are proposed. Firstly, a novel text orientation analysis method is described to analyze the orientation of original web postings and their replies. Secondly, an improved single-pass clustering algorithm is introduced to cluster the subject of web discussion and discover the hot topics.We also construct a prototype system, named WPSAS (web public sentiment analysis system), as experimental platform to validate the presented methodology. The experimental results show that our methods are effective and efficient.
Keywords
- web public sentiment
- orientation analys
How to Cite
Feng Zhao, Qianqiao Hu, Xiaolin Xu, Runxi Zeng, and Yi Lin, "Orientation Mining-Driven Approach to Analyze Web Public Sentiment," Journal of Software, vol. 6, no. 8, pp. 1417-1428, 2011. https://doi.org/ 10.4304/jsw.6.8.1417-1428
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).