Abstract
In this paper, we propose a system for aspect-based sentiment analysis (ABSA) by incorporating the concepts of multi-objective optimization (MOO), distributional thesaurus (DT) and unsupervised lexical induction. The task can be thought of as a sequence of processes such as aspect term extraction, opinion target expression identification and sentiment classification. We use MOO for selecting the most relevant features, and demonstrate that classification with the resulting feature set can improve classification accuracy on many datasets. As base learning algorithms we make use of Support Vector Machines (SVM) for sentiment classification and Conditional Random Fields (CRF) for aspect term and opinion target expression extraction tasks. Distributional thesaurus and unsupervised DT prove to be effective with enhanced performance. Experiments on benchmark setups of SemEval-2014 and SemEval-2016 shared tasks show that we achieve the state of the art on aspect-based sentiment analysis for several languages.
Keywords
- Sentiment analysis
- Aspect based sentiment analysis
- MOO
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Biemann, C.: Unsupervised part-of-speech tagging in the large. Res. Lang. Comput. 7(2–4), 101–135 (2009)
Biemann, C., Giuliano, C., Gliozzo, A.: Unsupervised part-of-speech tagging supporting supervised methods. In: Proceedings of RANLP, Borovets, Bulgaria, vol. 7, pp. 8–15 (2007)
Biemann, C., Riedl, M.: From global to local similarities: a graph-based contextualization method using distributional thesauri. In: Proceedings of the 8th Workshop on TextGraphs in Conjunction with EMNLP, Seattle, USA, pp. 39–43 (2013)
Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G.A., Reynar, J.: Building a sentiment summarizer for local service reviews. In: WWW Workshop on NLP in the Information Explosion Era, Beijing, China, vol. 14, pp. 339–348 (2008)
Deb, K.: Multi-objective Optimization using Evolutionary Algorithms, vol. 16. Wiley, Chichester (2001)
Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, New York, USA, pp. 231–240 (2008)
Ekbal, A., Saha, S.: Multiobjective optimization for classifier ensemble and feature selection: an application to named entity recognition. Int. J. Doc. Anal. Recogn. (IJDAR) 15(2), 143–166 (2012)
Ekbal, A., Saha, S.: Simulated annealing based classifier ensemble techniques: application to part of speech tagging. J. Inf. Fusion 14(3), 288–300 (2013)
Fahrni, A., Klenner, M.: Old wine or warm beer: target-specific sentiment analysis of adjectives. In: Proceedings of the Symposium on Affective Language in Human and Machine, AISB, Aberdeen, Scotland, pp. 60–63 (2008)
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics, Madrid, Spain, pp. 174–181 (1997)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, USA, pp. 168–177 (2004)
Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the 20th International Conference on Computational Linguistics, Geneva, pp. 1367–1373 (2004)
Kumar, A., Kohail, S., Kumar, A., Ekbal, A., Biemann, C.: IIT-TUDA at SemEval-2016 Task 5: beyond sentiment lexicon: combining domain dependency and distributional semantics features for aspect based sentiment analysis. In: 10th International Workshop on Semantics Evaluation (SemEval-2016), San Diego, USA, pp. 311–317. ACL (2016)
Lin, D.: Automatic retrieval and clustering of similar words. In: Proceedings of the 17th International Conference on Computational Linguistics, Stroudsburg, vol. 2, pp. 768–774 (1998)
Liu, B.: Sentiment Analysis and Opinion Mining, vol. 5. Morgan & Claypool Publishers, San Rafael (2012)
Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining, vol. 454. Springer Science & Business Media, Norwell (2012)
Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 17(4), 491–502 (2005)
Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Miller, T., Biemann, C., Zesch, T., Gurevych, I.: Using distributional similarity for lexical expansion in knowledge-based word sense disambiguation. In: Proceedings of COLING, Mumbai, India, pp. 1781–1796 (2012)
Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)
Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, Jeju Island, Korea, vol. 1, pp. 339–348 (2012)
Nielsen, F.Å.: A new ANEW: evaluation of a word list for sentiment analysis in microblogs. In: Proceedings of the ESWC 2011 Workshop on ‘Making Sense of Microposts’: Big Things Come in Small Packages, Heraklion, Greece, pp. 93–98 (2011)
Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., Al-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., Clercq, O.D., Hoste, V., Apidianaki, M., Tannier, X., Loukachevitch, N., Kotelnikov, E., Bel, N., Jimnez-Zafra, S.M., Eryiit, G.: Semeval-2016 Task 5: aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), San Diego, USA, pp. 19–30 (2016)
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: Semeval-2014 Task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Dublin, pp. 27–35 (2014)
Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Stroudsburg, USA, pp. 339–346 (2005)
Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2016)
Turney, P.D.: Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Philadelphia, USA, pp. 417–424 (2002)
Wiebe, J., Mihalcea, R.: Word sense and subjectivity. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, pp. 1065–1072 (2006)
Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Singapore, vol. 3, pp. 1533–1541 (2009)
Zhu, X., Kiritchenko, S., Mohammad, S.M.: NRC-Canada-2014: recent improvements in the sentiment analysis of tweets. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Dublin, Ireland, pp. 443–447 (2014)
Zhuang, L., Jing, F., Zhu, X.Y.: Movie review mining and summarization. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, Virginia, USA, pp. 43–50 (2006)
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Akhtar, M.S., Kohail, S., Kumar, A., Ekbal, A., Biemann, C. (2017). Feature Selection Using Multi-objective Optimization for Aspect Based Sentiment Analysis. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_2
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