Abstract
Sentiment analysis deals with inferring people’s sentiments and opinions from texts. An important aspect of sentiment analysis is polarity classification, which consists of inferring a document’s polarity – the overall sentiment conveyed by the text – in the form of a numerical rating. In contrast to existing approaches to polarity classification, we propose to take the authors of the documents into account. Specifically, we present a nearest-neighbour collaborative approach that utilises novel models of user similarity. Our evaluation shows that our approach improves on state-of-the-art performance, and yields insights regarding datasets for which such an improvement is achievable.
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References
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)
Pang, B., Lee, L.: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL), Ann Arbor, Michigan, pp. 115–124 (2005)
Snyder, B., Barzilay, R.: Multiple aspect ranking using the Good Grief algorithm. In: Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference (HLT/NAACL), Rochester, New York, pp. 300–307 (2007)
Gamon, M.: Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING), Geneva, Switzerland, pp. 841–847 (2004)
Lin, W., Wilson, T., Wiebe, J., Hauptmann, A.: Which side are you on? Identifying perspectives at the document and sentence levels. In: Proceedings of the 10th Conference on Natural Language Learning (CoNLL), New York, pp. 109–116 (2006)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)
Seroussi, Y., Zukerman, I., Bohnert, F.: A user-based approach to multi-way polarity classification. Technical Report 2010/253, Faculty of Information Technology, Monash University, Clayton, Victoria 3800, Australia (2010)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR ’99: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, California, pp. 230–237 (1999)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, Pennsylvania, pp. 79–86 (2002)
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Seroussi, Y., Zukerman, I., Bohnert, F. (2010). Collaborative Inference of Sentiments from Texts. In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_19
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DOI: https://doi.org/10.1007/978-3-642-13470-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13469-2
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