Abstract
The contents generated by netizens on the Web can reflect public sentiments to a great extent, so analyzing these contents is very useful for government agencies in guiding their public information, propaganda programs, and decision support. Because of the civilization diversity and economy difference, the netizens inhabiting or employing in different districts may have the different sentiments for the same topic or event. Analyzing the sentiment difference of different districts will help government agencies make more pertinent decision. However, current researches in this domain have less considered the opinion distribution on different districts. In this paper, we propose an approach of semi-automatic public sentiment analysis for opinion and district, which includes automatic data acquiring, sentiment modeling, opinion clustering, and district clustering, and manual threshold setting and result analysis. In detail, on the one hand, we group public sentiment into some opinion clusters by means of clustering technique. On the other hand, based on the opinion clusters, we further partition every opinion cluster on district into district opinion and analyze the result. Experiment results in Tencent comments show the feasibility and validity of our approach.
Project supported by the State Key Development Program for Basic Research of China (Grant No. 2011CB302200-G) and National Natural Science Foundation of China (Grant No. 60973019, 61100026).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet Allocation. Journal of Machine Learning Research (JMLR) 3, 993–1022 (2003)
Dong, Z., Dong, Q.: HowNet, http://www.keenage.com/html/e_index.html
Feng, S., Wang, D., Yu, G., Gao, W., Wong, K.: Extracting common emotions from blogs based on fine-grained sentiment clustering. Knowl. Inf. Syst. 27(2), 281–302 (2011)
Golaxy: ICTCLAS, http://www.ictclas.org
Khan, A., Baharudin, B., Khan, K.: Sentiment Classification from Online Customer Reviews Using Lexical Contextual Sentence Structure. In: Mohamad Zain, J., Wan Mohd, W.M.b., El-Qawasmeh, E. (eds.) ICSECS 2011, Part I. CCIS, vol. 179, pp. 317–331. Springer, Heidelberg (2011)
Ku, L., Chen, H.: Mining opinions from the Web: Beyond relevance retrieval. JASIST (JASIS) 58(12), 1838–1850 (2007)
Luo, Y., Lin, G., Fu, Y.: Finer Granularity Clustering for Opinion Mining. In: ISCID, pp. 68–71 (2009)
MacQueen, J.: Some methods for classification and analysis of multivariate observation. In: Proc. of 5th Berkeley Symp. Math. Statist. and Prob., vol. 1, pp. 281–297 (1967)
Melville, P., Gryc, W., Lawrence, R.: Sentiment analysis of blogs by combining lexical knowledge with text classification. In: KDD 2009, pp. 1275–1284 (2009)
Phan, X., Nguyen, M., Horiguchi, S.: Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In: WWW 2008, pp. 91–100 (2008)
Tan, S., Zhang, J.: An empirical study of sentiment analysis for Chinese documents. Expert Syst. Appl. (ESWA) 34(4), 2622–2629 (2008)
Tang, H., Tan, S., Cheng, X.: A survey on sentiment detection of reviews. Expert Syst. Appl. (ESWA) 36(7), 10760–10773 (2009)
Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: WWW 2008, pp. 111–120 (2008)
Wang, L., Yn, C., Jia, Y.: Toward Public Opinions Detection: Measuring the Similarity between Instant Messages. In: ODBIS 2008, pp. 21–28 (2008)
Wu, Q., Tan, S.: A two-stage framework for cross-domain sentiment classification. Expert Syst. Appl. (ESWA) 38(11), 14269–14275 (2011)
Ye, Q., Zhang, Z., Law, R.: Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst. Appl. (ESWA) 36(3), 6527–6535 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, D., Feng, S., Yan, C., Yu, G. (2012). An Approach of Semi-automatic Public Sentiment Analysis for Opinion and District. In: Wang, L., Jiang, J., Lu, J., Hong, L., Liu, B. (eds) Web-Age Information Management. WAIM 2011. Lecture Notes in Computer Science, vol 7142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28635-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-28635-3_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28634-6
Online ISBN: 978-3-642-28635-3
eBook Packages: Computer ScienceComputer Science (R0)