Abstract
The reviews of the products are increasing rapidly on the web due to the rapid growth and uses of the Internet. The products review makes very big impact on consumer’s interest in buying or not buying a product. However, there are various products, which have thousands of user-generated reviews. Mining this enormous online reviews and finding the important reviews for a user became a challenging task. It is very hard for consumers to find out the true quality of a particular product due to the presence of large number of reviews for a single product. To solve this problem, we are proposing a ranking mechanism which can be efficiently used to rank different reviews in accordance to their aspects rating. Here, the ranking mechanism uses the numerous ratings of the aspect and calculates the aggregate score of the review. This paper demonstrates the ranking of various reviews by means of their aspects rating through ranked voting method. Both the practicability and the benefits of the suggested approach are illustrated through an example.
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References
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing. Marcel Dekker, Inc., New York (2009)
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of 14th International Conference on WWW, pp. 342–351. Chiba, Japan (2005)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In Proceedings of SIGKDD, pp. 168–177. Seattle, WA, USA (2004)
Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: Proceedings of ACL, pp. 1533–1541, Singapore (2009)
Ohana, B., Tierney, B.: Sentiment classification of reviews using SentiWordNet. In: Proceedings IT&T Conference, Dublin, Ireland (2009)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of EMNLP, pp. 79–86. Philadelphia, PA, USA (2002)
Obata, T., Ishii, H.: A method for discriminating efficient candidates with ranked voting. Eur. J. Oper. Res. 151, 233–237 (2003)
Cook, W.D., Kress, M.: A data envelopment model for aggregating preference rankings. Manage. Sci. 36(11), 1302–1310 (1990)
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Kumar, R., Sharan, A., Yadav, C.S. (2016). A Framework for Ranking Reviews Using Ranked Voting Method. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 380. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2523-2_25
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DOI: https://doi.org/10.1007/978-81-322-2523-2_25
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