An Unsupervised Aspect Detection Model for Sentiment Analysis of Reviews

  • Ayoub Bagheri
  • Mohamad Saraee
  • Franciska de Jong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7934)


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.


sentiment analysis opinion mining aspect detection review mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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. 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)CrossRefGoogle Scholar
  5. 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. 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. 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. 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. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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. 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. 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)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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. 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. 17.
    Nakagawa, H., Mori, T.: Automatic Term Recognition based on Statistics of Compound Nouns and their Components. Terminology 9(2), 201–219 (2003)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 19.
    Yang, Y.: An evaluation of statistical approaches to text categorization. Inf. Retr. 1(1-2), 69–90 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ayoub Bagheri
    • 1
  • Mohamad Saraee
    • 2
  • Franciska de Jong
    • 3
  1. 1.Intelligent Database, Data Mining and Bioinformatics Lab, Electrical and Computer Engineering DepartmentIsfahan University of TechnologyIsfahanIran
  2. 2.School of Computing, Science and EngineeringUniversity of SalfordManchesterUK
  3. 3.Human Media InteractionUniversity of TwenteEnschedeThe Netherlands

Personalised recommendations