Abstract
With its ever growing amount of user-generated content, the Web has become a trove of consumer information. The free text format in which most of this content is written, however, prevents straightforward analysis. Instead, natural language processing techniques are required to quantify the textual information embedded within text. This research focuses on extracting the sentiment that can be found in consumer reviews. In particular, we focus on finding the sentiment associated with the various aspects of the product or service a consumer writes about. Using a standard Support Vector Machine for classification, we propose six different types of patterns: lexical, syntactical, synset, sentiment, hybrid, surface. We demonstrate that several of these lexico-syntactic patterns can be used to improve sentiment classification for aspects.
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References
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010), vol. 10, pp. 2200–2204 (2010)
Brychcín, T., Konkol, M., Steinberger, J.: UWB: machine learning approach to aspect-based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 817–822. Association for Computational Linguistics and Dublin City University (2014)
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics (ACL 1997), pp. 174–181. Morgan Kaufman Publishers and Association for Computational Linguistics (1997)
Kiritchenko, S., Zhu, X., Cherry, C., Mohammad, S.: NRC-Canada-2014: detecting aspects and sentiment in customer reviews. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 437–442. Association for Computational Linguistics and Dublin City University (2014)
Koto, F., Adriani, M.: The use of POS sequence for analyzing sentence pattern in Twitter sentiment analysis. In: Proceedings of the 29th IEEE International Conference on Advanced Information Networking and Applications Workshops (WAINA 2015), pp. 547–551. IEEE (2015)
Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: SemEval-2015 Task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495. Association for Computational Linguistics (2015)
Schouten, K., Frasincar, F.: The benefit of concept-based features for sentiment analysis. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds.) ESWC 2015. CCIS, vol. 548, pp. 223–233. Springer, Heidelberg (2015)
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The authors are partially supported by the Dutch national program COMMIT.
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Schouten, K. et al. (2016). Aspect-Based Sentiment Analysis Using Lexico-Semantic Patterns. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10042. Springer, Cham. https://doi.org/10.1007/978-3-319-48743-4_3
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DOI: https://doi.org/10.1007/978-3-319-48743-4_3
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