Abstract
Web Opinion Mining (WOM) is a new concept in Web Intelligence. It embraces the problem of extracting, analyzing and aggregating web data about opinions. Studying users’ opinions is relevant because through them it is possible to determine how people feel about a product or service and know how it was received by the market. In this chapter, we show an overview about what Opinion Mining is and give some approaches about how to do it. Also, we distinguish and discuss four resources from where opinions can be extracted from, analyzing in each case the main issues that could alter the mining process. One last interesting topic related to WOM and discussed in this chapter is the summarization and visualization of the WOM results.We consider these techniques to be important because they offer a real chance to understand and find a real value for a huge set of heterogeneous opinions collected. Finally, having given enough conceptual background, a practical example is presented using Twitter as a platform for Web Opinion Mining. Results show how an opinion is spread through the network and describes how users influence each other.
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References
Balbachan, F., Dell’Era, D.: Automatic sentiment analysis of short texts in twitter platform (2011) (in Spanish)
Bifet, A., Frank, E.: Sentiment Knowledge Discovery in Twitter Streaming Data. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds.) DS 2010. LNCS, vol. 6332, pp. 1–15. Springer, Heidelberg (2010)
Cardie, C., Wiebe, J., Wilson, T., Litman, D.: Combining low-level and summary representations of opinions for multi-perspective question answering. In: Proceedings of the AAAI Spring Symposium on New Directions in Question Answering, pp. 20–27 (2003)
Carenini, G., Ng, R., Pauls, A.: Multi-document summarization of evaluative text. In: Proceedings of the European Chapter of the Association for Computational Linguistics (EACL), pp. 305–312 (2006)
comScore/the Kelsey group. Online consumer-generated reviews have significant impact on offline purchase behavior. Press Release (November 2007), http://www.comscore.com/press/release.asp?press=1928
Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web, pp. 519–528. ACM (2003)
Harb, A., Plantié, M., Dray, G., Roche, M., Trousset, F., Poncelet, P.: Web opinion mining: How to extract opinions from blogs? In: Proceedings of the 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, pp. 211–217. ACM (2008)
Hsee, C., Hatfield, E., Carlson, J.G., Chemtob, C.: The effect of power on susceptibility to emotional contagion. Cognition and Emotion 4, 327–340 (2004)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)
Hu, M., Liu, B.: Opinion extraction and summarization on the web. In: Proceedings of the National Conference on Artificial Intelligence, vol. 21, p. 1621. AAAI Press, Menlo Park (2006)
Jurafsky, D., Martin, J.H., Kehler, A., Vander Linden, K., Ward, N.: Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition, vol. 2. Prentice-Hall, New Jersey (2000)
Ku, L.W., Liang, Y.T., Chen, H.H.: Opinion extraction, summarization and tracking in news and blog corpora. In: Proceedings of AAAI-2006 Spring Symposium on Computational Approaches to Analyzing Weblogs, vol. 2001 (2006)
Liu, B.: Web data mining: exploring hyperlinks, contents, and usage data. Springer (2011)
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th International Conference on World Wide Web, pp. 342–351. ACM (2005)
Nishikawa, H., Hasegawa, T., Matsuo, Y., Kikui, G.: Optimizing informativeness and readability for sentiment summarization. In: Proceedings of the ACL 2010 Conference Short Papers, pp. 325–330. Association for Computational Linguistics (2010)
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of LREC (2010)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1-2), 1–135 (2008)
Park, D.H., Kim, S.: The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electronic Commerce Research and Applications 7(4), 399–410 (2009)
Razorfish. Digital consumer behavior study (2007)
Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL Student Research Workshop, pp. 43–48. Association for Computational Linguistics (2005)
Seki, Y., Eguchi, K., Kando, N., Aono, M.: Opinion-focused summarization and its analysis at duc 2006. In: Proceedings of the Document Understanding Conference (DUC), pp. 122–130 (2006)
Shin, H.S., Hanssens, D.M., Kim, K.I., Gajula, B.: Impact of positive vs. negative e-sentiment on daily market value of high-tech products (2011)
Stoyanov, V., Cardie, C.: Partially supervised coreference resolution for opinion summarization through structured rule learning. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 336–344. Association for Computational Linguistics (2006)
Tateishi, K., Fukushima, T., Kobayashi, N., Takahashi, T., Fujita, A., Inui, K., Matsumoto, Y.: Web opinion extraction and summarization based on viewpoints of products (in japanese). Information Processing Society of Japan SIGNL Note 93, 1–8 (2004) (in Japanese)
Zhu, F., Zhang, X.: Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing 74(2), 133–148 (2010)
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Taylor, E.M., Rodríguez O., C., Velásquez, J.D., Ghosh, G., Banerjee, S. (2013). Web Opinion Mining and Sentimental Analysis. In: Velásquez, J., Palade, V., Jain, L. (eds) Advanced Techniques in Web Intelligence-2. Studies in Computational Intelligence, vol 452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33326-2_5
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DOI: https://doi.org/10.1007/978-3-642-33326-2_5
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