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

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 Twi...

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Bibliographic Details
Published in:TheScientificWorld 2015, Vol.2015 (2015), p.1-8
Main Authors: Ravichandran, M., Chellatamilan, T., Kulanthaivel, G.
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Classification
Humans
Instant messaging
Learning
Online education
Online social networks
Sentiment analysis
Social aspects
Social Media
Social network analysis
Social networks
Teaching
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Summary: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.
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2015/617358