Abstract
The phenomenon of big data makes managing, processing, and extracting valuable information from the Web an increasingly challenging task. As such, the abundance of user-generated content with opinions about products or brands requires appropriate tools in order to be able to capture consumer sentiment. Such tools can be used to aggregate content by means of sentiment summarization techniques, extracting text segments that reflect the overall sentiment of a text in a compressed form. We explore what features distinguish relevant from irrelevant text segments in terms of the extent to which they reflect the overall sentiment of conversational documents. In our empirical study on a collection of Dutch conversational documents, we find that text segments with opinions, segments with arguments supporting these opinions, segments discussing aspects of the subject of a text, and relatively long sentences are key indicators for text segments that summarize the sentiment conveyed by a text as a whole.
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
Bal, D., Bal, M., van Bunningen, A., Hogenboom, A., Hogenboom, F., Frasincar, F.: Sentiment Analysis with a Multilingual Pipeline. In: Bouguettaya, A., Hauswirth, M., Liu, L. (eds.) WISE 2011. LNCS, vol. 6997, pp. 129–142. Springer, Heidelberg (2011)
Beineke, P., Hastie, T., Manning, C., Vaithyanathan, S.: Exploring Sentiment Summarization. In: AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications, pp. 12–15. AAAI Press (2004)
Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G., Reynar, J.: Building a Sentiment Summarizer for Local Service Reviews. In: WWW 2008 Workshop on NLP Challenges in the Information Explosion Era (NLPIX 2008) (2008), http://www.cl.cs.titech.ac.jp/~fujii/NLPIX2008/paper3.pdf
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1), 10–18 (2009)
Hall, M., Smith, L.: Practical Feature Subset Selection for Machine Learning. In: 21st Australasian Computer Science Conference (ACSC 1998), pp. 181–191. Springer, Heidelberg (1998)
Lerman, K., Blair-Goldensohn, S., McDonald, R.: Sentiment Summarization: Evaluating and Learning User Preferences. In: 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2009), pp. 514–522. Association for Computational Linguistics (2009)
Madden, S.: From Databases to Big Data. IEEE Internet Computing 16(3), 4–6 (2012)
Mitchell, T.: Machine Learning. McGraw-Hill Series in Computer Science. McGraw-Hill (1997)
Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2(1), 1–135 (2008)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment Classification using Machine Learning Techniques. In: Empirical Methods in Natural Language Processing (EMNLP 2002), pp. 79–86. Association for Computational Linguistics (2002)
Pingdom: Internet 2011 in Numbers (2012), http://royal.pingdom.com/2012/01/17/internet-2011-in-numbers/
Popescu, A., Etzioni, O.: Extracting Product Features and Opinions from Reviews. In: Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT 2005), pp. 339–346. Association for Computational Linguistics (2005)
Titov, I., McDonald, R.: A Joint Model of Text and Aspect Ratings for Sentiment Summarization, pp. 308–316 (2008)
Zhu, J., Zhu, M., Wang, H., Tsou, B.: Aspect-Based Sentence Segmentation for Sentiment Summarization. In: 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion (TSA 2009), pp. 65–72. Association for Computing Machinery (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
Mangnoesing, G., van Bunningen, A., Hogenboom, A., Hogenboom, F., Frasincar, F. (2012). An Empirical Study for Determining Relevant Features for Sentiment Summarization of Online Conversational Documents. In: Wang, X.S., Cruz, I., Delis, A., Huang, G. (eds) Web Information Systems Engineering - WISE 2012. WISE 2012. Lecture Notes in Computer Science, vol 7651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35063-4_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-35063-4_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35062-7
Online ISBN: 978-3-642-35063-4
eBook Packages: Computer ScienceComputer Science (R0)