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Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest
المؤلفون المشاركون
Ravichandran, M.
Kulanthaivel, G.
Chellatamilan, T.
المصدر
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-8، 8ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-03-18
دولة النشر
مصر
عدد الصفحات
8
التخصصات الرئيسية
الطب البشري
تكنولوجيا المعلومات وعلم الحاسوب
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1078929
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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