ABSTRACT
Aspect-based opinion mining from online reviews has attracted a lot of attention recently. Given a set of reviews, the main task of aspect-based opinion mining is to extract major aspects of the items and to infer the latent aspect ratings from each review. However, users may have different preferences which might lead to different opinions on the same aspect of an item. Even if fine-grained aspect rating analysis is provided for each review, it is still difficult for a user to judge whether a specific aspect of an item meets his own expectation. In this paper, we study the problem of estimating personalized sentiment polarities on different aspects of the items. We propose a unified probabilistic model called Factorized Latent Aspect ModEl (FLAME), which combines the advantages of collaborative filtering and aspect based opinion mining. FLAME learns users' personalized preferences on different aspects from their past reviews, and predicts users' aspect ratings on new items by collective intelligence. Experiments on two online review datasets show that FLAME outperforms state-of-the-art methods on the tasks of aspect identification and aspect rating prediction.
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res.}, 3:993--1022, Mar. 2003. Google ScholarDigital Library
- S. P. Boyd and L. Vandenberghe. Convex optimization. Cambridge university press, 2004. Google ScholarCross Ref
- J. Eisenstein, A. Ahmed, and E. P. Xing. Sparse additive generative models of text. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pages 1041--1048, 2011.Google Scholar
- M. Hoffman, F. R. Bach, and D. M. Blei. Online learning for latent dirichlet allocation. In advances in neural information processing systems, pages 856--864, 2010.Google ScholarDigital Library
- M. Hu and B. Liu. Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '04, pages 168--177, New York, NY, USA, 2004. ACM. Google ScholarDigital Library
- Y. Jo and A. H. Oh. Aspect and sentiment unification model for online review analysis. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 815--824. ACM, 2011. Google ScholarDigital Library
- Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, 2009. Google ScholarDigital Library
- J. Lee, S. Bengio, S. Kim, G. Lebanon, and Y. Singer. Local collaborative ranking. In Proceedings of the 18th international conference on World wide web, 2014. Google ScholarDigital Library
- F. Li, C. Han, M. Huang, X. Zhu, Y.-J. Xia, S. Zhang, and H. Yu. Structure-aware review mining and summarization. In Proceedings of the 23rd International Conference on Computational Linguistics, pages 653--661. Association for Computational Linguistics, 2010. Google ScholarDigital Library
- B. Liu. Opinion mining and sentiment analysis. In Web Data Mining, pages 459--526. Springer, 2011.Google ScholarCross Ref
- B. Liu, M. Hu, and J. Cheng. Opinion observer: analyzing and comparing opinions on the web. In Proceedings of the 14th international conference on World Wide Web, WWW '05, pages 342--351, New York, NY, USA, 2005. ACM. Google ScholarDigital Library
- J. McAuley and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. In Recsys, 2013. Google ScholarDigital Library
- J. McAuley, J. Leskovec, and D. Jurafsky. Learning attitudes and attributes from multi-aspect reviews. In Data Mining (ICDM), 2012 IEEE 12th International Conference on, pages 1020--1025. IEEE, 2012. Google ScholarDigital Library
- S. Moghaddam and M. Ester. Ilda: interdependent lda model for learning latent aspects and their ratings from online product reviews. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, SIGIR '11, pages 665--674, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- S. Moghaddam and M. Ester. On the design of lda models for aspect-based opinion mining. In Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM '12, pages 803--812, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- S. Moghaddam and M. Ester. The flda model for aspect-based opinion mining: addressing the cold start problem. In Proceedings of the 22nd international conference on World Wide Web, WWW '13, pages 909--918, 2013. Google ScholarDigital Library
- J. Nocedal. Updating quasi-newton matrices with limited storage. Mathematics of computation, 35(151):773--782, 1980.Google ScholarCross Ref
- G. Ronning. Maximum likelihood estimation of dirichlet distributions. Journal of statistical computation and simulation, 32(4):215--221, 1989.Google Scholar
- R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Advances in neural information processing systems, pages 1257--1264, 2007.Google Scholar
- I. Titov and R. McDonald. Modeling online reviews with multi-grain topic models. In Proceedings of the 17th international conference on World Wide Web, pages 111--120. ACM, 2008. Google ScholarDigital Library
- C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pages 448--456, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- H. Wang, Y. Lu, and C. Zhai. Latent aspect rating analysis on review text data: a rating regression approach. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 783--792. ACM, 2010. Google ScholarDigital Library
- H. Wang, Y. Lu, and C. Zhai. Latent aspect rating analysis without aspect keyword supervision. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 618--626. ACM, 2011. Google ScholarDigital Library
- Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu, and S. Ma. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th international ACM SIGIR conference on Research and development in Information Retrieval, SIGIR '14, Gold Coast, Australia, 2014. ACM. Google ScholarDigital Library
- W. X. Zhao, J. Jiang, H. Yan, and X. Li. Jointly modeling aspects and opinions with a maxent-lda hybrid. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP '10, pages 56--65, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics. Google ScholarDigital Library
Index Terms
- FLAME: A Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering
Recommendations
Collaborative filtering with weighted opinion aspects
Collaborative filtering (CF) aims to produce recommendations based on other users' ratings to an item. Most existing CF methods rely on the overall ratings an item has received. However, these ratings alone sometimes cannot provide sufficient ...
Collaborative Filtering with Aspect-Based Opinion Mining: A Tensor Factorization Approach
ICDM '12: Proceedings of the 2012 IEEE 12th International Conference on Data MiningCollaborative filtering (CF) aims to produce user specific recommendations based on other users' ratings of items. Most existing CF methods rely only on users' overall ratings of items, ignoring the variety of opinions users may have towards different ...
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
TSA '09: Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinionIn this paper we show that the extraction of opinions from free-text reviews can improve the accuracy of movie recommendations. We present three approaches to extract movie aspects as opinion targets and use them as features for the collaborative ...
Comments