Feature Specific Sentiment Analysis for Product Reviews

  • Subhabrata Mukherjee
  • Pushpak Bhattacharyya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7181)


In this paper, we present a novel approach to identify feature specific expressions of opinion in product reviews with different features and mixed emotions. The objective is realized by identifying a set of potential features in the review and extracting opinion expressions about those features by exploiting their associations. Capitalizing on the view that more closely associated words come together to express an opinion about a certain feature, dependency parsing is used to identify relations between the opinion expressions. The system learns the set of significant relations to be used by dependency parsing and a threshold parameter which allows us to merge closely associated opinion expressions. The data requirement is minimal as this is a one time learning of the domain independent parameters. The associations are represented in the form of a graph which is partitioned to finally retrieve the opinion expression describing the user specified feature. We show that the system achieves a high accuracy across all domains and performs at par with state-of-the-art systems despite its data limitations.


Latent Dirichlet Allocation Sentiment Analysis Target Feature Product Review Sentiment Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Mosha, C.: Combining Dependency Parsing with Shallow Semantic Analysis for Chinese Opinion-Element Relation Identification, pp. 299–305. IEEE (2010) Google Scholar
  2. 2.
    Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase Dependency Parsing for Opinion Mining. In: EMNLP 2009, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 3 (2009) Google Scholar
  3. 3.
    Zhang, Q., Wu, Y., Li, T., Ogihara, M., Johnson, J., Huang, X.: Mining Product Reviews Based on Shallow Dependency Parsing. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009 (2009) Google Scholar
  4. 4.
    Lakkaraju, H., Bhattacharyya, C., Bhattacharya, I., Merugu, S.: Exploiting Coherence for the simultaneous discovery of latent facets and associated sentiments. In: SIAM International Conference on Data Mining (SDM) (April 2011) Google Scholar
  5. 5.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: KDD 2004: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2004) Google Scholar
  6. 6.
    Griffiths, T.L., Steyvers, M., Blei, D.M., Tenenbaum, J.B.: Integrating Topics and Syntax. In: Advances in Neural Information Processing Systems, vol. 17 (2005) Google Scholar
  7. 7.
    Blei, D.M., Jordan, M., Ng, A.: Latent Dirichlet Allocation. Journal of Machine Learning and Research, 993–1022 (2003) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Subhabrata Mukherjee
    • 1
  • Pushpak Bhattacharyya
    • 1
  1. 1.Dept. of Computer Science and EngineeringIIT BombayIndia

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