Abstract
Aspect recognition and clustering is important for many sentiment analysis tasks. To date, many algorithms for recognizing product aspects have been explored, however, limited work have been done for clustering the product aspects. In this paper, we focus on the problem of product aspect clustering. Two effective aspect relations: relevant aspect relation and irrelevant aspect relation are proposed to describe the relationships between two aspects. According to these two relations, we can explore many relevant and irrelevant aspects into two different sets as background knowledge to describe each product aspect. Then, a hierarchical clustering algorithm is designed to cluster these aspects into different groups, in which aspect similarity computation is conducted with the relevant aspect set and irrelevant aspect set of each product aspect. Experimental results on camera domain demonstrate that the proposed method performs better than the baseline without using the two aspect relations, and meanwhile proves that the two aspect relations are effective.
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References
Bloom, K., Garg, N., Argamon, S.: Extracting appraisal expressions. In: HLT-NAACL 2007, pp. 308–315 (2007)
Branavan, S., Chen, H., Eisenstein, J., Barzilay, R.: Learning document-level semantic properties from free-text annotations. In: Proceedings of ACL 2008: HLT, pp. 263–271 (2008)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of KDD 2004, pp. 168–177 (2004)
Jakob, N., Gurevych, I.: Extracting opinion targets in a single- and cross-domain setting with conditional random fields. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, pp. 1035–1045. Association for Computational Linguistics, Stroudsburg (2010)
Li, S., Wang, R., Zhou, G.: Opinion target extraction using a shallow semantic parsing framework. In: AAAI, pp. 1671–1677 (2012)
Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers (2012)
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of WWW 2005, pp. 342–351 (2005)
Liu, K., Xu, L., Zhao, J.: Opinion target extraction using word-based translation model. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, pp. 1346–1356. Association for Computational Linguistics, Stroudsburg (2012)
Liu, K., Xu, L., Zhao, J.: Syntactic patterns versus word alignment: Extracting opinion targets from online reviews. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 1754–1763. Association for Computational Linguistics, Sofia (2013)
Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 339–348. Association for Computational Linguistics, Jeju Island (2012)
Mukherjee, A., Liu, B.: Modeling review comments. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 320–329. Association for Computational Linguistics, Jeju Island (2012), http://www.aclweb.org/anthology/P12-1034
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1-2), 1–135 (2008)
Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Computational Linguistics 37(1), 9–27 (2011)
Reschke, K., Vogel, A., Jurafsky, D.: Generating recommendation dialogs by extracting information from user reviews. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (vol. 2: Short Papers), pp. 499–504. Association for Computational Linguistics, Sofia (2013), http://www.aclweb.org/anthology/P13-2089
Sauper, C., Haghighi, A., Barzilay, R.: Content models with attitude. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, HLT 2011, vol. 1, pp. 350–358. Association for Computational Linguistics, Stroudsburg (2011)
Sauper, C., Haghighi, A., Barzilay, R.: Content models with attitude. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 350–358. Association for Computational Linguistics, Portland (2011), http://www.aclweb.org/anthology/P11-1036
Titov, I., McDonald, R.: A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of ACL 2008: HLT, pp. 308–316. Association for Computational Linguistics, Columbus (2008)
Wei, W., Gulla, J.A.: Sentiment learning on product reviews via sentiment ontology tree. In: ACL, pp. 404–413 (2010)
Woodsend, K., Lapata, M.: Multiple aspect summarization using integer linear programming. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 233–243. Association for Computational Linguistics, Jeju Island (2012), http://www.aclweb.org/anthology/D12-1022
Yu, J., Zha, Z.J., Wang, M., Chua, T.S.: Aspect ranking: Identifying important product aspects from online consumer reviews. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, HLT 2011, vol. 1, pp. 1496–1505. Association for Computational Linguistics, Stroudsburg (2011)
Zhai, Z., Liu, B., Xu, H., Jia, P.: Clustering product features for opinion mining. In: Proceedings of WSDM, pp. 347–354 (2011)
Zhao, Y., Qin, B., Hu, S., Liu, T.: Generalizing syntactic structures for product attribute candidate extraction. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 377–380. Association for Computational Linguistics, Los Angeles (2010)
Zhao, Y., Qin, B., Liu, T.: Sentiment analysis. Journal of Software 21(8), 1834–1848 (2010)
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Zhao, Y., Qin, B., Liu, T. (2014). Clustering Product Aspects Using Two Effective Aspect Relations for Opinion Mining. In: Sun, M., Liu, Y., Zhao, J. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2014 2014. Lecture Notes in Computer Science(), vol 8801. Springer, Cham. https://doi.org/10.1007/978-3-319-12277-9_11
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DOI: https://doi.org/10.1007/978-3-319-12277-9_11
Publisher Name: Springer, Cham
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