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An Unsupervised Aspect Detection Model for Sentiment Analysis of Reviews

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7934)

Abstract

With the rapid growth of user-generated content on the internet, sentiment analysis of online reviews has become a hot research topic recently, but due to variety and wide range of products and services, the supervised and domain-specific models are often not practical. As the number of reviews expands, it is essential to develop an efficient sentiment analysis model that is capable of extracting product aspects and determining the sentiments for aspects. In this paper, we propose an unsupervised model for detecting aspects in reviews. In this model, first a generalized method is proposed to learn multi-word aspects. Second, a set of heuristic rules is employed to take into account the influence of an opinion word on detecting the aspect. Third a new metric based on mutual information and aspect frequency is proposed to score aspects with a new bootstrapping iterative algorithm. The presented bootstrapping algorithm works with an unsupervised seed set. Finally two pruning methods based on the relations between aspects in reviews are presented to remove incorrect aspects. The proposed model does not require labeled training data and can be applicable to other languages or domains. We demonstrate the effectiveness of our model on a collection of product reviews dataset, where it outperforms other techniques.

Keywords

  • sentiment analysis
  • opinion mining
  • aspect detection
  • review mining

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References

  1. Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Computational Linguistics 37(1), 9–27 (2011)

    CrossRef  Google Scholar 

  2. Thet, T.T., Na, J.C., Khoo, C.S.G.: Aspect-Based Sentiment Analysis of Movie Reviews on Discussion Boards. Journal of Information Science 36(6), 823–848 (2010)

    CrossRef  Google Scholar 

  3. Hu, M., Liu, B.: Mining opinion features in customer reviews. In: American Association for Artificial Intelligence (AAAI) Conference, pp. 755–760 (2004)

    Google Scholar 

  4. Wei, C.P., Chen, Y.M., Yang, C.S., Yang, C.C.: Understanding what concerns consumers: A semantic approach to product feature extraction from consumer reviews. Information Systems and E-Business Management 8(2), 149–167 (2010)

    CrossRef  Google Scholar 

  5. Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, California, pp. 804–812 (2010)

    Google Scholar 

  6. Popescu, A., Etzioni, O.: Extracting product features and opinions from reviews. In: Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, pp. 339–346 (2005)

    Google Scholar 

  7. Yi, J., Nasukawa, T., Bunescu, R., Niblack, W.: Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In: 3rd IEEE International Conference on Data Mining (ICDM 2003), Melbourne, FL, pp. 427–434 (2003)

    Google Scholar 

  8. Somprasertsri, G., Lalitrojwong, P.: Automatic product feature extraction from online product reviews using maximum entropy with lexical and syntactic features. In: IEEE International Conference on Information Reuse and Integration, pp. 250–255 (2008)

    Google Scholar 

  9. Zhu, J., Wang, H., Zhu, M., Tsou, B.K.: Aspect-based opinion polling from customer reviews. IEEE Transactions on Affective Computing 2(1), 37–49 (2011)

    CrossRef  Google Scholar 

  10. Zhai, Z., Liu, B., Xu, H., Jia, P.: Constrained LDA for Grouping Product Features in Opinion Mining. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part I. LNCS, vol. 6634, pp. 448–459. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  11. Su, Q., Xu, X., Guo, H., Guo, Z., Wu, X., Zhang, X., Su, Z.: Hidden sentiment association in chinese web opinion mining. In: 17th International Conference on World Wide Web, Beijing, China, pp. 959–968 (2008)

    Google Scholar 

  12. Moghaddam, S., Ester, M.: ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews. In: 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 665–674. ACM (2011)

    Google Scholar 

  13. Fu, X., Liu, G., Guo, Y., Wang, Z.: Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems 37, 186–195 (2013)

    CrossRef  Google Scholar 

  14. Lin, C., He, Y., Everson, R., Ruger, S.: Weakly supervised joint sentiment-topic detection from text. IEEE Transaction on Knowledge & Data Engineering 24(6), 1134–1145 (2012)

    CrossRef  Google Scholar 

  15. Marcus, M., Santorini, B., Marcinkiewicz, M.A.: Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics 19(2), 313–330 (1993)

    Google Scholar 

  16. Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In: Proceedings of HLT-NAACL, pp. 252–259 (2003)

    Google Scholar 

  17. Nakagawa, H., Mori, T.: Automatic Term Recognition based on Statistics of Compound Nouns and their Components. Terminology 9(2), 201–219 (2003)

    CrossRef  Google Scholar 

  18. Yoshida, M., Nakagawa, H.: Automatic Term Extraction Based on Perplexity of Compound Words. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 269–279. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  19. Yang, Y.: An evaluation of statistical approaches to text categorization. Inf. Retr. 1(1-2), 69–90 (1999)

    CrossRef  Google Scholar 

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Bagheri, A., Saraee, M., de Jong, F. (2013). An Unsupervised Aspect Detection Model for Sentiment Analysis of Reviews. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2013. Lecture Notes in Computer Science, vol 7934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38824-8_12

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  • DOI: https://doi.org/10.1007/978-3-642-38824-8_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38823-1

  • Online ISBN: 978-3-642-38824-8

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