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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/617358 |
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| author | M. Ravichandran G. Kulanthaivel T. Chellatamilan |
| author_facet | M. Ravichandran G. Kulanthaivel T. Chellatamilan |
| author_sort | M. Ravichandran |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7c2b74e9126941faa418823af80e80f0 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-7c2b74e9126941faa418823af80e80f02025-02-03T05:50:56ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/617358617358Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of InterestM. Ravichandran0G. Kulanthaivel1T. Chellatamilan2Department of Computer Science and Engineering, Sathyabama University, Tamil Nadu 600119, IndiaEducational Media Centre, NITTTR, Chennai 600113, IndiaDepartment of Computer Science and Engineering, Arunai Engineering College, Tiruvannamalai 606603, IndiaEvery 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.http://dx.doi.org/10.1155/2015/617358 |
| spellingShingle | M. Ravichandran G. Kulanthaivel T. Chellatamilan Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest The Scientific World Journal |
| title | Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest |
| title_full | Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest |
| title_fullStr | Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest |
| title_full_unstemmed | Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest |
| title_short | Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest |
| title_sort | intelligent topical sentiment analysis for the classification of e learners and their topics of interest |
| url | http://dx.doi.org/10.1155/2015/617358 |
| work_keys_str_mv | AT mravichandran intelligenttopicalsentimentanalysisfortheclassificationofelearnersandtheirtopicsofinterest AT gkulanthaivel intelligenttopicalsentimentanalysisfortheclassificationofelearnersandtheirtopicsofinterest AT tchellatamilan intelligenttopicalsentimentanalysisfortheclassificationofelearnersandtheirtopicsofinterest |