Identifying Sentiment-Based Contradictions
Resumo
Contradiction Analysis is a relatively new multidisciplinary and complex area with the main goal of identifying contradictory pieces of text. It can be addressed from the perspectives of different research areas such as Natural Language Processing, Opinion Mining, Information Retrieval, and Information Extraction. This paper focuses on the problem of detecting sentiment-based contradictions which occur in the sentences of a given review text. Unlike other types of contradictions, the detection of sentiment-based contradictions can be tackled as a post-processing step in the traditional sentiment analysis task. In this context, we adapted and extended an existing contradiction analysis framework by filtering its results to remove the reviews that are erroneously labeled as contradictory. The filtering method is based on two simple term similarity algorithms. An experimental evaluation on real product reviews has shown proportional improvements of up to 30% in classification accuracy and 26% in the precision of contradiction detection.
Palavras-chave:
Contradiction detection, Sentiment-based
Referências
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de Marneffe, M., Rafferty, A. N., and Manning, C. D. (2008). Finding contradictions in text. In ACL 2008, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, June 15-20, 2008, Columbus, Ohio, USA, pages 1039–1047.
Dori-Hacohen, S. and Allan, J. (2015). Automated Controversy Detection on the Web, pages 423–434.
Ennals, R., Byler, D., Agosta, J. M., and Rosario, B. (2010a). What is disputed on the web? In Proceedings of the 4th workshop on Information credibility, pages 67–74. ACM.
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Galley, M., McKeown, K., Hirschberg, J., and Shriberg, E. (2004). Identifying agreement and disagreement in conversational speech: Use of bayesian networks to model pragmatic dependencies. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, page 669.
Harabagiu, S., Hickl, A., and Lacatusu, F. (2006). Negation, contrast and contradiction in text processing. In AAAI, volume 6, pages 755–762.
Hillard, D., Ostendorf, M., and Shriberg, E. (2003). Detection of agreement vs. disagreement in meetings: Training with unlabeled data. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003–short papers-Volume 2, pages 34–36.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1):1–167.
Liu, B., Li, X., Lee, W. S., and Yu, P. S. (2004). Text classification by labeling words. In AAAI, volume 4, pages 425–430.
Meeker, M. (2015). Internet trends 2015-code conference. Glokalde, 1(3).
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Padó, S., de Marneffe, M.-C., MacCartney, B., Rafferty, A. N., Yeh, E., and Manning, C. D. (2008). Deciding entailment and contradiction with stochastic and edit distance-based alignment. In Proceedings of the 1st Text Analysis Conference (TAC’08).
Ritter, A., Downey, D., Soderland, S., and Etzioni, O. (2008). It’s a contradiction—no, it’s not: a case study using functional relations. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 11–20.
Sangani, C. and Ananthanarayanan, S. (2013). Sentiment analysis of app store reviews. Technical report, Stanford University.
Socher, R., Perelygin, A., Wu, J. Y., Chuang, J., Manning, C. D., Ng, A. Y., and Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the conference on empirical methods in natural language processing (EMNLP), volume 1631, page 1642.
Suarez Vargas, D. and Moreira, V. (2015). Detecting contrastive sentences for sentiment analysis. In Proceedings of the Brazilian Symposium on Databases.
Tsytsarau, M. and Palpanas, T. (2012). Survey on mining subjective data on the web. Data Mining and Knowledge Discovery, 24(3):478–514.
Tsytsarau, M., Palpanas, T., and Denecke, K. (2011). Scalable detection of sentiment-based contradictions. In First international workshop on knowledge diversity on the Web, Colocated with WWW 2011.
de Marneffe, M., Rafferty, A. N., and Manning, C. D. (2008). Finding contradictions in text. In ACL 2008, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, June 15-20, 2008, Columbus, Ohio, USA, pages 1039–1047.
Dori-Hacohen, S. and Allan, J. (2015). Automated Controversy Detection on the Web, pages 423–434.
Ennals, R., Byler, D., Agosta, J. M., and Rosario, B. (2010a). What is disputed on the web? In Proceedings of the 4th workshop on Information credibility, pages 67–74. ACM.
Ennals, R., Trushkowsky, B., and Agosta, J. M. (2010b). Highlighting disputed claims on the web. In Proceedings of the 19th international conference on World wide web, pages 341–350.
Galley, M., McKeown, K., Hirschberg, J., and Shriberg, E. (2004). Identifying agreement and disagreement in conversational speech: Use of bayesian networks to model pragmatic dependencies. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, page 669.
Harabagiu, S., Hickl, A., and Lacatusu, F. (2006). Negation, contrast and contradiction in text processing. In AAAI, volume 6, pages 755–762.
Hillard, D., Ostendorf, M., and Shriberg, E. (2003). Detection of agreement vs. disagreement in meetings: Training with unlabeled data. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003–short papers-Volume 2, pages 34–36.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1):1–167.
Liu, B., Li, X., Lee, W. S., and Yu, P. S. (2004). Text classification by labeling words. In AAAI, volume 4, pages 425–430.
Meeker, M. (2015). Internet trends 2015-code conference. Glokalde, 1(3).
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Padó, S., de Marneffe, M.-C., MacCartney, B., Rafferty, A. N., Yeh, E., and Manning, C. D. (2008). Deciding entailment and contradiction with stochastic and edit distance-based alignment. In Proceedings of the 1st Text Analysis Conference (TAC’08).
Ritter, A., Downey, D., Soderland, S., and Etzioni, O. (2008). It’s a contradiction—no, it’s not: a case study using functional relations. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 11–20.
Sangani, C. and Ananthanarayanan, S. (2013). Sentiment analysis of app store reviews. Technical report, Stanford University.
Socher, R., Perelygin, A., Wu, J. Y., Chuang, J., Manning, C. D., Ng, A. Y., and Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the conference on empirical methods in natural language processing (EMNLP), volume 1631, page 1642.
Suarez Vargas, D. and Moreira, V. (2015). Detecting contrastive sentences for sentiment analysis. In Proceedings of the Brazilian Symposium on Databases.
Tsytsarau, M. and Palpanas, T. (2012). Survey on mining subjective data on the web. Data Mining and Knowledge Discovery, 24(3):478–514.
Tsytsarau, M., Palpanas, T., and Denecke, K. (2011). Scalable detection of sentiment-based contradictions. In First international workshop on knowledge diversity on the Web, Colocated with WWW 2011.
Publicado
04/10/2016
Como Citar
VARGAS, Danny Suarez; MOREIRA, Viviane.
Identifying Sentiment-Based Contradictions. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 31. , 2016, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 76-87.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2016.24310.
