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The Scientific World Journal
Volume 2015, Article ID 617358, 8 pages
http://dx.doi.org/10.1155/2015/617358
Research Article

Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest

1Department of Computer Science and Engineering, Sathyabama University, Tamil Nadu 600119, India
2Educational Media Centre, NITTTR, Chennai 600113, India
3Department of Computer Science and Engineering, Arunai Engineering College, Tiruvannamalai 606603, India

Received 18 October 2014; Revised 9 January 2015; Accepted 10 February 2015

Academic Editor: Lifei Chen

Copyright © 2015 M. Ravichandran et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [3 citations]

The following is the list of published articles that have cited the current article.

  • S. Shubha, and P. Suresh, “An efficient machine Learning Bayes Sentiment Classification method based on review comments,” 2017 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), pp. 1–6, . View at Publisher · View at Google Scholar
  • Farman Ali, Daehan Kwak, Pervez Khan, S.M. Riazul Islam, Kye Hyun Kim, and K.S. Kwak, “Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling,” Transportation Research Part C: Emerging Technologies, vol. 77, pp. 33–48, 2017. View at Publisher · View at Google Scholar
  • Sameerchand Pudaruth, Sharmila Moheeputh, Narmeen Permessur, and Adeelah Chamroo, “Sentiment Analysis from Facebook Comments using Automatic Coding in NVivo 11,” ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, vol. 7, no. 1, pp. 41, 2018. View at Publisher · View at Google Scholar