Abstract
As virtual utterances of opinions or sentiment are becoming increasingly abundant on the Web, automated ways of analyzing sentiment in such data are becoming more and more urgent. In this paper, we provide a classification scheme for existing approaches to document sentiment analysis. As the role of negations in sentiment analysis has been explored only to a limited extent, we additionally investigate the impact of taking into account negation when analyzing sentiment. To this end, we utilize a basic sentiment analysis framework – consisting of a wordbank creation part and a document scoring part – taking into account negation. Our experimental results show that by accounting for negation, precision on human ratings increases with 1.17%. On a subset of selected documents containing negated words, precision increases with 2.23%.
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Bautin, M., Vijayarenu, L., Skiena, S.: International Sentiment Analysis for News and Blogs. In: 2nd International Conference on Weblogs and Social Media (ICWSM 2008), pp. 19–26. AAAI Press, Menlo Park (2008)
Benamara, F., Cesarano, C., Reforgatio, D.: Sentiment Analysis: Adjectives and Adverbs are better than Adjectives Alone. In: 1st International Conference on Weblogs and Social Media (ICWSM 2007), pp. 203–206. AAAI Press, Menlo Park (2007)
Cesarano, C., Dorr, B., Picariello, A., Reforgiato, D., Sagoff, A., Subrahmanian, V.: OASYS: An Opinion Analysis System. In: AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs (CAAW 2006), pp. 21–26. AAAI Press, Menlo Park (2006)
Choi, Y., Cardie, C.: Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis. In: 13th Conference on Empirical Methods in Natural Language Processing (EMNLP 2008), pp. 793–801. ACL (2008)
Ding, X., Lu, B., Yu, P.S.: A Holistic Lexicon-Based Approach to Opinion Mining. In: 1st ACM International Conference on Web Search and Web Data Mining (WSDM 2008), pp. 231–240. ACM, New York (2008)
Fellbaum, C.D.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
Hu, M., Liu, B.: Mining and Summarizing Customer Reviews. In: 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), pp. 168–177. ACM, New York (2004)
Jia, L., Yu, C., Meng, W.: The Effect of Negation on Sentiment Analysis and Retrieval Effectiveness. In: 18th ACM Conference on Information and Knowledge Management (CIKM 2009), pp. 1827–1830. ACM, New York (2009)
Jindal, N., Liu, B.: Identifying Comparative Sentences in Text Documents. In: 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2006), pp. 244–251. ACM, New York (2006)
Lerman, K., Blair-Goldensohn, S., McDonald, R.: Sentiment Summarization: Evaluating and Learning User Preferences. In: 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 514–522. ACL (2009)
Liu, B.: Web Data Mining. Springer, Heidelberg (2007)
Liu, B.: Handbook of Natural Language Processing. In: Sentiment Analysis and Subjectivity, 2nd edn., pp. 627–667. CRC Press, Boca Raton (2010)
Morante, R., Liekens, A., Daelemans, W.: Learning the Scope of Negation in Biomedical Texts. In: 13th Conference on Empirical Methods in Natural Language Processing (EMNLP 2008), pp. 715–724. ACL (2008)
Morante, R., Daelemans, W.: A Metalearning Approach to Processing the Scope of Negation. In: Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009), pp. 21–29. ACL (2009)
Wiebe, J.M.: Learning Subjective Adjectives from Corpora. In: 17th National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence (AAAI 2000), pp. 735–740. AAAI Press, Menlo Park (2000)
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Heerschop, B., van Iterson, P., Hogenboom, A., Frasincar, F., Kaymak, U. (2011). Analyzing Sentiment in a Large Set of Web Data While Accounting for Negation. In: Mugellini, E., Szczepaniak, P.S., Pettenati, M.C., Sokhn, M. (eds) Advances in Intelligent Web Mastering – 3. Advances in Intelligent and Soft Computing, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18029-3_20
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DOI: https://doi.org/10.1007/978-3-642-18029-3_20
Publisher Name: Springer, Berlin, Heidelberg
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