Abstract
This study leverages the syntactic, semantic and contextual features of online hotel and restaurant reviews to extract information aspects and summarize them into meaningful feature groups. We have designed a set of syntactic rules to extract aspects and their descriptors. Further, we test the precision of a modified algorithm for clustering aspects into closely related feature groups, on a dataset provided by Yelp.com. Our method uses a combination of semantic similarity methods- distributional similarity, co-occurrence and knowledge base based similarity, and performs better than two state-of-the-art approaches. It is shown that opinion words and the context provided by them can prove to be good features for measuring the semantic similarity and relationship of their product features. Our approach successfully generates thematic aspect groups about food quality, décor and service quality.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of Human Language Technology Conference/Conference on Empirical Methods in Natural Language Processing, HLT-EMNLP 2005, Vancouver, CA, pp. 339–346 (2005)
Miller, G.A.: WordNet: A Lexical Database for English Comm. ACM 38(11), 39–41 (1995)
Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Computational Linguistics 37, 9–27 (2011)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of ACM-KDD, pp.168–177 (2004)
Yi, J., Niblack, W.: Sentiment mining in WebFountain. In: Proceedings of the 21st International Conference on Data Engineering, ICDE 2005, pp. 1073–1083. IEEE Computer Society, Washington, DC (2005)
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, vol. 3, pp. 1533–1541 (2009)
Liu, B., Hu, M., Cheng, J.: Opinion Observer: Analyzing and Comparing Opinions on the Web. In: Proceedings of WWW, pp. 342–351 (2005)
Carenini, G., Ng, R., Zwart, E.: Extracting knowledge from evaluative text. In: Proceedings of International Conference on Knowledge Capture, pp. 11–18 (2005)
Guo, H., Zhu, H., Guo, Z., Zhang, X.X., Su, Z.: Product Feature Categorization with Multilevel Latent Semantic Association. In: Proceedings of International Conference on Information and Knowledge Management, pp.1087–1096 (2009)
Matsuo, Y., Sakaki, T., Uchiyama, K., Ishizuka, M.: Graph based word clustering using a web search engine. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP 2006, pp. 542–550. Association for Computational Linguistics, Stroudsburg (2006)
Blei, D.M., Ng, A.Y., Michael, J.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, pp. 111–120 (2008)
Titov, I., McDonald, R.: A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of ACL, HLT, pp. 308–316 (2008)
Kim, S., Zhang, J., Chen, Z., Oh, A., Liu, S.: A Hierarchical Aspect-Sentiment Model for Online Reviews. In: Proceedings of AAAI, pp. 526–533 (2013)
Liu, K., Xu, L., Zhao, J.: Syntactic Patterns versus Word Alignment: Extracting Opinion Targets from Online Reviews. In: Proceedings of ACL 2013, pp. 1754–1763 (2013)
Mukherjee, A., Liu, B.: Aspect Extraction through Semi-Supervised Modeling. In: Proceedings of ACL, pp. 339–348 (2012)
Liu, Q., Gao, Z., Liu, B., Zhang, Y.: A Logic Programming Approach to Aspect Extraction in Opinion Mining. In: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), WI-IAT 2013, vol. 01, pp. 276–283. IEEE Computer Society, Washington, DC (2013)
Hoang, H.H., Kim, S.N., Kan, M.Y.: A re-examination of lexical association measures. In: Proceedings of Workshop on Multiword Expressions (ACL-IJCNLP), pp. 31–39 (2009)
Li, Y.H., Bandar, Z., McLean, D.: An Approach for Measuring Semantic Similarity Using Multiple Information Sources. IEEE Trans. Knowledge and Data Eng. 15(4), 871–882 (2003)
Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28(1), 11–21 (1972)
Rada, M., Courtney, C., Carlo, S.: Corpus-based and knowledge-based measures of text semantic similarity. In: Proceedings of the 21st National Conference on Artificial Intelligence, vol. 1, pp. 775–780 (2006)
Dongen, S.V.: A cluster algorithm for graphs, Ph.D. dissertation, University of Utrecht (2000)
Zhai, Z., Liu, B., Xu, H., Jia, P.: Clustering product features for opinion mining. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining, pp. 347–354 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Gupta, P., Kumar, S., Jaidka, K. (2015). Summarizing Customer Reviews through Aspects and Contexts. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-18117-2_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18116-5
Online ISBN: 978-3-319-18117-2
eBook Packages: Computer ScienceComputer Science (R0)