Abstract
This paper presents our experimental work on analysis of sentiments and mood from a large number of Weblogs (blog posts) on two interesting topics namely ‘Women’s Reservation in India’ and ‘Regionalism’. The experimental work involves transforming the collected blog data into vector space representation, doing Parts of Speech Tagging to extract opinionated words and then applying semantic orientation approach based SO-PMI-IR algorithm for mining the sentiment and mood information contained in the blog text. We obtained interesting results, which have been successfully evaluated for correctness through both manual tagging and by cross-validating the outcomes with other machine learning techniques. The results demonstrate that these analytical schemes can be successfully used for blog post analysis in addition to the review texts. The paper concludes with a short discussion of relevance of the work and its applied perspective.
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
Technorati Blogosphere Statistics, http://technorati.com/blogging/state-of-the-blogosphere/
Google Blog Search, http://google.com/help/about_blogsearch.html
Alag, S.: Collective Intelligence in Action. Manning, New York (2009)
Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Penn Treebank Project, http://www.cis.upenn.edu/~treebank/home.html
Dave, K., Lawerence, S., Pennock, D.: Mining the Peanut Gallery-Opinion Extraction and Semantic Classification of Product Reviews. In: 12th International World Wide Web Conference, pp. 519–528. ACM Press, New York (2003)
Turney, P.: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: ACL 2002, 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, US, pp. 417–424 (2002)
Esuli, A., Sebastiani, F.: Determing the Semantic Orientation of Terms Through Gloss Analysis. In: CIKM 2005, 14th ACM International Conference on Information and Knowledge Management, Bremen, DE, pp. 617–624 (2005)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment Classification using Machine Learning Techniques. In: Conference on Empirical Methods in Natural Language Processing, Philadelphia, US, pp. 79–86 (2002)
Kim., S.M., Hovy, E.: Determining Sentiment of Opinions. In: COLING Conference, Geneva (2004)
Durant, K.T., Smith, M.D.: Mining Sentiment Classification from Political Web Logs. In: WebKDD 2006, ACM Press, New York (2006)
Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, Berlin (2008)
SentiWordNet, http://patty.isti.cnr.it/~esuli/software/SentiWordNet
Turney, P., Littman, M.L.: Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word corpus. NRC Publications Archive (2002)
Esuli, A., Sebastiani, F.: SentiWordNet: A Publicly available Lexical Resource for Opinion Mining. In: Fifth Conference on Language Resources and Evaluation (LREC 2006), Geneva (2006)
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Mishne, G., Rijke, M.D.: MoodViews: Tools for Blog Mood Analysis. In: AAAI 2006 Spring Symposium on Computational Approaches to Analyzing Weblogs (AAAI-CAAW 2006) (March 2006)
Balog, K., Rijke, M.D.: Decomposing Bloggers’ Moods. In: 3rd Annual Workshop on the Web blogging Ecosystem, At WWW 2006 (2006)
Mishne, G.: Experiments with Mood Classification in Blog Posts. In: 2005 Stylistic Analysis of Text for Information Access Conference (2005)
Uclassify Mood Analysis Tool, http://www.uclassify.com/browse/prfekt/Mood (retrieved April 2009)
Singh, V.K.: Mining the blogosphere for sociological inferences. In: Ranka, S., Banerjee, A., Biswas, K.K., Dua, S., Mishra, P., Moona, R., Poon, S.-H., Wang, C.-L. (eds.) IC3 2010. CCIS, vol. 94, pp. 547–558. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Singh, V.K., Mukherjee, M., Mehta, G.K. (2011). Sentiment and Mood Analysis of Weblogs Using POS Tagging Based Approach. In: Aluru, S., et al. Contemporary Computing. IC3 2011. Communications in Computer and Information Science, vol 168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22606-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-22606-9_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22605-2
Online ISBN: 978-3-642-22606-9
eBook Packages: Computer ScienceComputer Science (R0)