A comprehensive survey of research based on extraction of opinion words and opinion targets from customer reviews

  • Authors

    • Harminder Kaur
    • Pritam Bandyopadhyay
    • Sowmiya B
    2018-03-19
    https://doi.org/10.14419/ijet.v7i2.8.10522
  • .

  • Abstract

    This survey paper categorizes, compares, and summarizes the algorithms, data sets and performance measurement in the published articles related to extraction of opinion targets and words from customer reviews. The systems reviewed either deploy a supervised or completely unsupervised algorithm for the process. Most of the systems rely on K-nearest neighbor algorithm or a bootstrapping approach. Most of the methods have produced a list comprising of opinion targets from the customer reviews of a product. As a result, opinion targets usually are product features or attributes. The approaches mentioned in the papers reviewed suffer either from error propagation or from lack of automation for parsing long span relations. Certain approaches have taken an initial bag of seed words and proceeded to exploit the syntactical relationship between opinion words and targets. Mainly online product reviews have been used. It is generally believed that the co- occurrence of certain target and opinion words in close proximity makes them more relevant to each other and to the product as well. Owing to the nature of online review data sets most literature have not include assessment of opinion targets and words across multiple domains.

  • References

    1. [1] K. Schouten, F. Frasincar, and F. de Jong, “COMMIT-P1WP3: A co- occurrence based approach to aspect-level sentiment analysis,†in Proc. 8th Int. Workshop Semantic Eval. (SemEval), Dublin, Ireland, 2014, pp. 203–207

      [2] G. Qiu, L. Bing, J. Bu, and C. Chen, “Opinion word expansion and target extraction through double propagation,†Comput. Linguistics, vol. 37, no. 1, pp. 9–27, 2011.

      [3] M. Pontiki et al., “SemEval-2014 Task 4: Aspect based sentiment analysis,†in Proc. 8th Int. Workshop Semantic Eval. (SemEval), Dublin, Ireland, 2014, pp. 27–35.

      [4] K. Schouten and F. Frasincar, “Survey on aspect-level sentiment analysis,†IEEE Trans. Knowl. Data Eng., vol. 28, no. 3, pp. 813–830, Mar. 2016.

      [5] Yu Zhang and Weixiang Zhu. 2013. Extracting Implicit Features in Online Customer Reviews for Opinion Mining. In Proceedings of the 22nd International Conference on World Wide Web Companion (WWW 2013 Companion), pages 103–104. International World Wide Web Conferences Steering Committee.

      [6] Z. Hai, K. Chang, and J.-J. Kim, “Implicit feature identiï¬cation via co- occurrence association rule mining,†in Proc. 12th Int. Conf. Comput. Linguist. Intell. Text Process. (CICLing), Tokyo, Japan, 2011, pp. 393– 404.

      [7] F. Crestani, “Application of spreading activation techniques in information retrieval,†Artif. Intell. Rev., vol. 11, no. 6, pp. 453–482, 1997.

      [8] Z. Hai, K. Chang, and J.-J. Kim, “Implicit feature identiï¬cation via co- occurrence association rule mining,†in Proc. 12th Int. Conf. Comput. Linguist. Intell. Text Process. (CICLing), Tokyo, Japan, 2011, pp. 393– 404.

      [9] S. Kiritchenko, X. Zhu, C. Cherry, and S. M. Mohammad, “NRCCananda-2014: Detecting aspects and sentiment in customer reviews,†in Proc. 8th Int. Workshop Semantic Eval. (SemEval), Dublin, Ireland, 2014, pp. 437–442.

      [10] C. R. C. Brun, D. N. Popa, and C. Roux, “XRCE: Hybrid classiï¬cation for aspect-based sentiment analysis,†in Proc. 8th Int. Workshop Semantic Eval. (SemEval), Dublin, Ireland, 2014, pp. 838–842.

      [11] G. Castellucci, S. Filice, D. Croce, and R. Basili, “UNITOR: Aspect based sentiment analysis with structured learning,†in Proc. 8th Int. Workshop Semantic Eval. (SemEval), Dublin, Ireland, 2014, pp. 761–767.

      [12] T. Padmapriya and V.Saminadan, “Improving Performance of Downlink LTE-Advanced Networks Using Advanced Networks Using Advanced feedback Mechanisms and SINR Modelâ€, International Conference on Emerging Technology (ICET), vol.7, no.1, pp: 93, March 2014.

      [13] S.V.Manikanthan and K.Baskaran “Low Cost VLSI Design Implementation of Sorting Network for ACSFD in Wireless Sensor Networkâ€, CiiT International Journal of Programmable Device Circuits and Systems,Print: ISSN 0974 – 973X & Online: ISSN 0974 – 9624, Issue : November 2011, PDCS112011008.

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  • How to Cite

    Kaur, H., Bandyopadhyay, P., & B, S. (2018). A comprehensive survey of research based on extraction of opinion words and opinion targets from customer reviews. International Journal of Engineering & Technology, 7(2.8), 567-569. https://doi.org/10.14419/ijet.v7i2.8.10522

    Received date: 2018-03-23

    Accepted date: 2018-03-23

    Published date: 2018-03-19