Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest
Joint Authors
Ravichandran, M.
Kulanthaivel, G.
Chellatamilan, T.
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-18
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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: (1) lexicon based sentiment polarity of tweet messages; (2) the bigram cooccurrence relationship using naïve Bayesian; (3) 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.
American Psychological Association (APA)
Ravichandran, M.& Kulanthaivel, G.& Chellatamilan, T.. 2015. Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest. The Scientific World Journal،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1078929
Modern Language Association (MLA)
Kulanthaivel, G.…[et al.]. Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest. The Scientific World Journal No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1078929
American Medical Association (AMA)
Ravichandran, M.& Kulanthaivel, G.& Chellatamilan, T.. Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest. The Scientific World Journal. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1078929
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1078929