Orientation mining-driven approach to analyze web public sentiment

被引：3

作者：

Zhao F. ^{[1
]}

Hu Q. ^{[1
]}

Xu X. ^{[2
]}

Zeng R. ^{[2
]}

Lin Y. ^{[3
]}

机构：

[1] School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan

[2] College of Public Administration, Huazhong University of Science and Technology, Wuhan

[3] Department of Mathematics, Nanjing University, Nanjin

来源：

Journal of Software | 2011年 / 6卷 / 08期

关键词：

Clustering; Opinion mining; Orientation analysis; VSM; Web public sentiment;

D O I：

10.4304/jsw.6.8.1417-1428

中图分类号：

学科分类号：

摘要：

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. © 2011 ACADEMY PUBLISHER.

引用

页码：1417 / 1428

页数：11

共 26 条

[1]

Mei Q., Lingn X., Wondra M., Hang S., Zhai C., Topic Sentiment Mixture: Modeling Facets and Opinions In Weblogs, Proceedings of 2007 International World Wide Web Conference (WWW 2007), pp. 171-180, (2007)

[2]

Topic Detection and Tracking: Eventbased Information Organization, (2002)

[3]

Haibin M., Web public sentiment and analyzing technology, Guang Ming Daily

[4]

Yi J., Nasukawa T., Bunescu R., Niblack W., Extracting Sentiments About a Given Topic Using Natural Language Processing Techniques, Proceedings of the third IEEE International Conference on Data Mining (ICDM 2003), pp. 427-434, (2003)

[5]

Turney Peter D., Thumbs UP Or Thumbs Down? Semantic Orientation Applied to UnsuPervised Classification of Reviews, Procedings of the 40th Annual Meetion of the Association for Computational Liguistic, pp. 417-424, (2002)

[6]

Li D., Laurent A., Roche M., Pascal Poncelet. Extraction of Opposite Sentiments In Classified Free Format Text Reviews, Proceedings of 19th International Conference on Database and Expert Systems Applications, pp. 710-717, (2008)

[7]

Yanlan Z., Jin M., Yaqian Z., Xuanjing H., Li-De W., Semantic Orientation Computing Based on HowNet, Journal of Chinese Information Processing, 20, 1, pp. 14-20, (2006)

[8]

Bo P., Lee L., Vaithyanathan S., Thumbs Up? Sentiment Classification Using Machine Learning Techniques, Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, pp. 79-86, (2002)

[9]

Bo P., Lee L., Seeing Stars: Exploiting Class Relationships For Sentiment Categorization With Respect to Rating Scales, Proceedings of the Association for Computational Linguistics, pp. 115-124, (2005)

[10]

Turney P., Littman M., Measuring praise and criticism: Inference of semantic orientation from association, ACM Transactions On Information Systems, 21, 4, pp. 315-346, (2003)

← 1 2 3 →