Learning Text Patterns to Detect Opinion Targets

Filipa Peleja, João Magalhães

Abstract

Exploiting sentiment relations to capture opinion targets has recently caught the interest of many researchers. In many cases target entities are themselves part of the sentiment lexicon creating a loop from which it is difficult to infer the overall sentiment to the target entities. In the present work we propose to detect opinion targets by extracting syntactic patterns from short-texts. Experiments show that our method was able to successfully extract 1,879 opinion targets from a total of 2,052 opinion targets. Furthermore, the proposed method obtains comparable results to SemEval 2015 opinion target models in which we observed the syntactic structure relation that exists between sentiment words and their target.

References

  1. Albornoz, J. C., Chugur, I., and Amigó, E. (2012). Using an Emotion-based Model and Sentiment Analysis Techniques to Classify Polarity for Reputation. In Forner, P., Karlgren, J., and Womser-Hacker, C., editors, Conference and Labs of the Evaluation Forum, Online Working Notes (CLEF), volume 1178.
  2. Baccianella, S., Esuli, A., and Sebastiani, F. (2010). SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC), 25:2200-2204.
  3. Bespalov, D., Bai, B., Shokoufandeh, A., and Qi, Y. (2011). Sentiment Classification Based on Supervised Latent n-gram Analysis. Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM), pages 375-382.
  4. Bollen, J. (2010). Determining the public mood state by analysis of microblogging posts. Alife XII Conf. MIT Press, page 667.
  5. Brown, P. F., Desouza, P. V., Mercer, R. L., Pietra, V. J. D., and Lai, J. C. (1992). Class-based n-gram models of natural language. Computational linguistics, 18(4):467-479.
  6. Clark, A. (2003). Combining distributional and morphological information for part of speech induction. In Proceedings of the tenth conference on European chapter of the Association for Computational LinguisticsVolume 1, pages 59-66. Association for Computational Linguistics.
  7. Diakopoulos, N. and Shamma, D. (2010). Characterizing debate performance via aggregated twitter sentiment. In Proceedings of the 28th international conference on Human factors in computing systems, pages 1195- 1198.
  8. Esuli, A. and Sebastiani, F. (2006). Sentiwordnet: A publicly available lexical resource for opinion mining. Proceedings of the 5th Conference on Language Resources and Evaluation (LREC), 6:417-422.
  9. Ghorbel, H. and Jacot, D. (2010). Sentiment Analysis of French Movie Reviews. Advances in Distributed Agent-Based Retrieval Tools, 4th International Workshop on Distributed Agent-based Retrieval Tools (DART), Springer Heidelberg, pages 97-108.
  10. Gildea, D. and Jurafsky, D. (2002). Automatic Labeling of Semantic Roles. Computational Linguistics, MIT Press Linguistics, 28(3):245-288.
  11. Go, A., Bhayani, R., and Huang, L. (2009). Twitter Sentiment Classification using Distant Supervision. CS224N Project Technical report, Stanford, pages 1- 12.
  12. Hatzivassiloglou, V. and Wiebe, J. M. (2000). Effects of adjective orientation and gradability on sentence subjectivity. Proceedings of the 18th Conference on Computational Linguistics (COLING), 1:299-305.
  13. Heerschop, B., Goossen, F., Hogenboom, A., Frasincar, F., Kaymak, U., and De Jong, F. (2011). Polarity analysis of texts using discourse structure. Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM), pages 1061-1070.
  14. Hu, M. and Liu, B. (2004). Mining opinion features in customer reviews. Proceedings of the Association for the Advancement of Artificial Intelligence 19th International Conference on Artifical Intelligence (AAAI), pages 755-760.
  15. Kim, S.-M. and Hovy, E. (2006). Extracting opinions, opinion holders, and topics expressed in online news media text. Proceedings of the Workshop on Sentiment and Subjectivity in Text, pages 1-8.
  16. Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of Natural Language Processing, CRC Press, Taylor and Francis Group.
  17. Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, Morgan and Claypool Publishers, pages 1-167.
  18. Meena, A. and Prabhakar, T. V. (2007). Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis. In Amati, G., Carpineto, C., and Romano, G., editors, Proceedings of the 29th European Conference on Advances in Information Retrieval (ECIR), volume 4425, pages 573-580.
  19. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111- 3119.
  20. Moshfeghi, Y., Piwowarski, B., and Jose, J. M. (2011). Handling data sparsity in collaborative filtering using emotion and semantic based features. Proceedings of the 34th international ACM conference on Research and development in Information Retrieval (SIGIR), pages 625-634.
  21. Pak, A. and Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. Proceedings of the International Conference on Language Resources and Evaluation (LREC), 10:1320-1326.
  22. Pang, B. and Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. Proceedings of the Association of Computational Linguistics (ACL), pages 271- 278.
  23. Pang, B. and Lee, L. (2005). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, 43(1):115-124.
  24. Pang, B., Lee, L., and Vaithyanathan, S. (2002). Thumbs up?: sentiment classification using machine learning techniques. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 10:79-86.
  25. Pontiki, M., Galanis, D., Papageogiou, H., Manandhar, S., and Androutsopoulos, I. (2015). Semeval-2015 task 12: Aspect based sentiment analysis. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado.
  26. Qu, L., Ifrim, G., and Weikum, G. (2010). The bag-ofopinions method for review rating prediction from sparse text patterns. Proceedings of the 23rd International Conference on Computational Linguistics (COLING), pages 913-921.
  27. Rao, D. and Ravichandran, D. (2009). Semi-supervised Polarity Lexicon Induction. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (COLING), EACL 7809, pages 675-682, Stroudsburg, PA, USA. Association for Computational Linguistics.
  28. Riloff, E. and Wiebe, J. (2003). Learning extraction patterns for subjective expressions. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 105-112.
  29. Turney, P. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (ACL), pages 417-424.
  30. Turney, P. D. (2001). Mining the Web for Synonyms: PMIIR versus LSA on TOEFL. Proceedings of the 12th European Conference on Machine Learning (EMCL), 2167:491-502.
  31. Wiebe, J. M. (1994). Tracking point of view in narrative. Journal of Computational Linguistics, MIT Press Cambridge, 20(2):233-287.
  32. Wiebe, J. M., Bruce, R. F., and O'Hara, T. P. (1999). Development and use of a gold-standard data set for subjectivity classifications. Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics (ACL), pages 246- 253.
  33. Wilson, T., Wiebe, J., and Hoffmann, P. (2009). Recognizing Contextual Polarity: An Exploration of Features for Phrase-level Sentiment Analysis. Journal Computational Linguistics, 35(3):399-433.
Download


Paper Citation


in Harvard Style

Peleja F. and Magalhães J. (2015). Learning Text Patterns to Detect Opinion Targets . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8, pages 337-343. DOI: 10.5220/0005612603370343


in Bibtex Style

@conference{kdir15,
author={Filipa Peleja and João Magalhães},
title={Learning Text Patterns to Detect Opinion Targets},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},
year={2015},
pages={337-343},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005612603370343},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - Learning Text Patterns to Detect Opinion Targets
SN - 978-989-758-158-8
AU - Peleja F.
AU - Magalhães J.
PY - 2015
SP - 337
EP - 343
DO - 10.5220/0005612603370343