Classification and analysis of users review using different classification techniques in intelligent e-learning system Online publication date: Wed, 09-Sep-2020
by Aditya Khamparia; Sanjay Kumar Singh; Ashish Kr. Luhach; Xiao-Zhi Gao
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 13, No. 2/3/4, 2020
Abstract: The internet comprised of a large number of data in the form of text, images, stickers, etc. which is also called as reviews or feedbacks created by users to share their expressions or knowledge. All those data may be in a different kind like positive, negative or neutral, and sometimes it may be in a single word or a single sentence or in document form. There are a few techniques, which are measured to provide better classifier like classification-support vector machine (SVM), Naïve Bayes (NB) and KNN. Using 'word tokenizer' in the techniques like (SVM, Naïve, KNN, J48, DT) it is compared with different results (accuracy, sensitivity, specificity, ppv and npv). It has been observed that after using various tokenizer in Weka tool (alphabetic tokenizer) has provided better results in measure, i.e., SVM (86.39%) comparing to techniques and specificity (83.77%) in average comparing to other measures.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook