Efficient Analysis of User Reviews and Community-Contributed Photographs for Reputation Generation

  • V. SubramaniyaswamyEmail author
  • Logesh Ravi
  • V. Indragandhi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)


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.


Sentiment analysis Prediction Decision making Opinion mining Reputation generation Data mining 



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).


  1. 1.
    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)Google Scholar
  2. 2.
    Ahluwalia, R.: Examination of psychological processes underlying resistance to persuasion. J. Consum. Res. 27(2), 217–232 (2000)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Aral, S.: The Problem With online ratings (2013).
  5. 5.
    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)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    Shapiro, C.: Consumer information, product quality, and seller reputation. Bell J. Econ. 13, 20–35 (1982)CrossRefGoogle Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Zhou, X., Wan, X., Xiao, J.: CMiner: opinion extraction and summarization for Chinese microblogs. IEEE Trans. Knowl. Data Eng. 28(7), 1650–1663 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • V. Subramaniyaswamy
    • 1
    Email author
  • Logesh Ravi
    • 2
  • V. Indragandhi
    • 3
  1. 1.School of ComputingSASTRA Deemed UniversityThanjavurIndia
  2. 2.Sri Ramachandra faculty of Engineering and TechnologySri Ramachandra Institute of Higher Education and ResearchChennaiIndia
  3. 3.School of Electrical EngineeringVellore Institute of TechnologyVelloreIndia

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