Abstract
People can share their thoughts and opinions on any entities through the Internet. Normally, the attitude of the preferences of human can be predicted which are expressed in natural languages. Using sentimental mining method, the readership predictions are made on online reviews of locations. The reviews have been useful for the travelers to gain knowledge about the information of various locations and shortlist the best that is needed for them. In this paper, we categorize the locations based on the reviews and community-contributed photographs with the help of yelp and Tripadvisor datasets. In the proposed approach, opinions are filtered to eliminate unrelated ones through opinion pertinence calculation, and later grouped into a number of fused principal opinion sets. Based on the experiments conducted on large-scale datasets, the proposed approach is found to be useful for the user to make a decision.
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References
Abdel-Hafez, A., Xu, Y.: An accurate rating aggregation method for generating item reputation. In: IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–8 (2015)
Ahluwalia, R.: Examination of psychological processes underlying resistance to persuasion. J. Consum. Res. 27(2), 217–232 (2000)
Angst, C.M., Agarwal, R.: Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion. MIS Q. 33(2), 339–370 (2009)
Aral, S.: The Problem With online ratings (2013). http://sloanreview.mit.edu/article/the-problem-with-online-ratings-2/2013
Kim, S.M., Hovy, E.: Extracting opinions, opinion holders, and topics expressed in online news media text. In: Proceedings of the Workshop on Sentiment and Subjectivity in Text, pp. 1–8. Association for Computational Linguistics (2006)
Logesh, R., Subramaniyaswamy, V., Malathi, D., Sivaramakrishnan, N., Vijayakumar, V.: Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. In: Neural Computing and Applications (2019)
Shapiro, C.: Consumer information, product quality, and seller reputation. Bell J. Econ. 13, 20–35 (1982)
Shri, J.M.R., Subramaniyaswamy, V.: An effective approach to rank reviews based on relevance by weighting method. Indian J. Sci. Technol. 8(11) (2015)
Wang, J.Z., Yan, Z., Yang, L.T., Huang, B.X.: An approach to rank reviews by fusing and mining opinions based on review pertinence. Inf. Fusion 23, 3–15 (2015)
Weng, Y., Zhao, L.: A blogger reputation evaluation model based on opinion analysis. In: IEEE, Asia-Pacific Services Computing Conference (APSCC), pp. 27–34 (2010)
Zhou, X., Wan, X., Xiao, J.: CMiner: opinion extraction and summarization for Chinese microblogs. IEEE Trans. Knowl. Data Eng. 28(7), 1650–1663 (2016)
Acknowledgements
The authors are grateful to Science and Engineering Research Board (SERB), Department of Science & Technology, New Delhi, for the financial support (No. YSS/2014/000718/ES).
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Subramaniyaswamy, V., Ravi, L., Indragandhi, V. (2020). Efficient Analysis of User Reviews and Community-Contributed Photographs for Reputation Generation . In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore. https://doi.org/10.1007/978-981-15-0184-5_2
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DOI: https://doi.org/10.1007/978-981-15-0184-5_2
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