Abstract
In sentiment analysis of reviews we focus on classifying the polarity (positive, negative) of conveyed opinions from the perspective of textual evidence. Most of the work in the field has been intensively applied on the English language and only few experiments have explored other languages. In this paper, we present a supervised classification of French movie reviews where sentiment analysis is based on some shallow linguistic features such as POS tagging, chunking and simple negation forms. In order to improve classification, we extracted word semantic orientation from the lexical resource SentiWordNet. Since SentiWordNet is an English resource, we apply a word-translation from French to English before polarity extraction. Our approach is evaluated using French movie reviews. Obtained results showed that shallow linguistic features has significantly improved the classification performance with respect to the bag of words baseline.
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References
Andreevskaia, A., Bergler, S.: Mining wordnet for fuzzy sentiment: Sentiment tag extraction from wordnet. In: Proceedings of Conference of the European Chapter of the Association for Computational Linguistics, EACL 2006 (2006)
Abbasi, A., Chen, H., Salem, A.: Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Transactions on Information Systems (TOIS) 26(3), Article 12 (June 2008)
Pal, C., Wei, B.: Cross lingual adaptation: an experiment on sentiment classifications. In: Proceedings of the ACL 2010 Conference Short Papers, pp. 258–262 (2010)
Denecke, K.: Using sentiwordnet for multilingual sentiment analysis. In: Proceedings of the IEEE International Conference on Data Engineering (ICDE 2008), pp. 507–512 (2008)
Esuli, A., Sebastiani, F.: Determining the semantic orientation of terms through gloss classification. In: Proceedings of CIKM 2005, pp. 617–624 (2005)
Esuli, A., Sebastiani, F.: Sentiwordnet: a publicly available lexical resource for opinion mining. In: In Proceedings of the 5th Conference on Language Resources and Evaluation LREC, vol. 6 (2006)
Gamon, M., Aue, A.: Automatic identification of sentiment vocabulary: exploiting low association with known sentiment terms. In: Proceedings of the ACL 2005 Workshop on Feature Engineering for Machine Learning in Natural Language Processing. Association for Computational Linguistics (July 2005)
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, pp. 611–617 (August 2004)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of Knowledge Discovery and Data Mining, KDD 2004 (2004)
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the 8th conference on European Chapter of the Association for Computational Linguistics, pp. 174–181 (1997)
Hassan, A., Radev, D.: Identifying text polarity using random walks. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 395–403 (2010)
Mokken, R.J., Kamps, J., Marx, M., de Rijke, M.: Sentiwordnet: a publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation, LREC 2004, vol. 4, pp. 1115–1118 (2004)
Joachims, T.: Making large-scale svm learning practical. ACM Transactions on Information Systems, TOIS (1998)
Booth, R.J., Pennebaker, J.W., Francis, M.E.: Linguistic Inquiry and Word Count (LIWC): LIWC 2001. Erlbaum Publisher, Mahwah (2001)
Kim, S.-M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING 2004), pp. 1367–1373 (August 2004)
Matsumoto, S., Takamura, H., Okumura, M.: Sentiment classification using word sub-sequences and dependency sub-trees. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 301–311. Springer, Heidelberg (2005)
Nastase, V., Sokolova, M., Shirabad, J.S.: Do happy words sound happy? a study of the relation between form and meaning for english words expressing emotions. In: Proceedings of Recent Advances in Natural Language Processing (RANLP 2007), pp. 406–410 (2007)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (July 2002)
Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Proceedings of the International Conference on New Methods in Language Processing, pp. 44–49 (1994)
Strapparava, C., Valitutti, A.: Wordnet-affect: an affective extension of wordnet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004), pp. 1083–1086 (May 2004)
Takamura, H., Inui, T., Okumura, M.: Extracting semantic orientations of words using spin model. In: Association of Computational Linguistics ACL 2005, pp. 133–140 (2005)
Turney, P.D., Littman, M.L.: Unsupervised learning of semantic orientation from a hundred-billion-word corpus. Technical Report ERB-1094, National Research Council Canada, Institute for Information Technology (2002)
Turney, P.D., Littman, M.L.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS), 315–346 (2003)
Thet, T.T., Na, J.-C., Khoo, C., Shakthikumar, S.: Sentiment analysis of movie reviews on discussion boards using a linguistic approach. In: Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement (2009)
Tang, H., Tan, S., Cheng, X.: A survey on sentiment detection of reviews. Expert Systems with Applications: An International Journal 36(7), 10760–10773 (2009)
Turney, P.D.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002), pp. 417–424 (2002)
Whissell, C.M.: The dictionary of affect in language. In: Lutchik, R., Kellerman, H. (eds.) Emotion: Theory, Research, and Experience, pp. 113–131 (1989)
Wiegand, M., Klakow, D.: The role of knowledge-based features in polarity classification at sentence level. In: Proceedings of the Florida Artificial Intelligence Research Society Conference (FLAIRS Conference 2009) (2009)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2005), pp. 347–354 (October 2005)
Yu, H., Hatzivassiloglou, V.: The potts model. Reviews of Modern Physics 4(1), 135–268 (1982)
Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, EMNLP 2003 (2003)
Yi, J., Nasukawa, T., Bunescu, R., Niblack, W.: Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In: The Third IEEE International Conference on Data Mining (2003)
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Ghorbel, H., Jacot, D. (2011). Sentiment Analysis of French Movie Reviews. In: Pallotta, V., Soro, A., Vargiu, E. (eds) Advances in Distributed Agent-Based Retrieval Tools. Studies in Computational Intelligence, vol 361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21384-7_7
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DOI: https://doi.org/10.1007/978-3-642-21384-7_7
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