Title: Classification and analysis of users review using different classification techniques in intelligent e-learning system
Authors: Aditya Khamparia; Sanjay Kumar Singh; Ashish Kr. Luhach; Xiao-Zhi Gao
Addresses: Department of Computer Science and Engineering, Lovely Professional University, Jalandhar, Punjab, India ' Department of Computer Science and Engineering, Lovely Professional University, Jalandhar, Punjab, India ' Department of Electrical and Communication Engineering, The PNG University of Technology, Papua New Guinea ' School of Computing, University of Eastern Finland, Kuopio, Finland
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.
Keywords: support vector machine; SVM; sentiment analysis; opinion mining; supervised learning; KNN; Naïve.
International Journal of Intelligent Information and Database Systems, 2020 Vol.13 No.2/3/4, pp.139 - 149
Received: 18 Apr 2019
Accepted: 04 Jul 2019
Published online: 25 Aug 2020 *