Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2015 (2015), Article ID 617358, 8 pages
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.


Every day, huge numbers of instant tweets (messages) are published on Twitter as it is one of the massive social media for e-learners interactions. The options regarding various interesting topics to be studied are discussed among the learners and teachers through the capture of ideal sources in Twitter. The common sentiment behavior towards these topics is received through the massive number of instant messages about them. In this paper, rather than using the opinion polarity of each message relevant to the topic, authors focus on sentence level opinion classification upon using the unsupervised algorithm named bigram item response theory (BIRT). It differs from the traditional classification and document level classification algorithm. The investigation illustrated in this paper is of threefold which are listed as follows: lexicon based sentiment polarity of tweet messages; the bigram cooccurrence relationship using naïve Bayesian; the bigram item response theory (BIRT) on various topics. It has been proposed that a model using item response theory is constructed for topical classification inference. The performance has been improved remarkably using this bigram item response theory when compared with other supervised algorithms. The experiment has been conducted on a real life dataset containing different set of tweets and topics.