Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Student Feedback Sentiment Analysis System for Distance Education using Arm with K-Means Clustering


Affiliations
1 Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, India
     

   Subscribe/Renew Journal


The rapid development of Internet has resulted in the boom of evaluations about products and services. For extracting the aspects and determining the opinions from reviews Sentiment Analysis is used. The main challenges faced by Sentiment Analysis system is that, in order to increase or decrease the market value of the product the spammers may post irrelevant or fake reviews and another challenge deals with the classification of both Implicit and Explicit features present among the review sentences in the dataset. The proposed system deals with the identification of fake reviews through fake review Indicators which help in removing the fake reviews. For the better identification of both implicit and explicit features, association rule mining with K-Means Clustering is used. Lexicon method is used for the classification of sentiments into positive and negative polarities. The advantage of the proposed system is that the fake reviews can be detected and eliminated in the dataset and both implicit and explicit attribute extraction from the review sentence can be identified along with its polarities through Lexicon based Method.

Keywords

Lexicon Based Method, Sentiment Analysis, Spammers, K-Means Clustering.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Sujata Rani and Parteek Kumar, “A Sentiment Analysis System to Improve Teaching and Learning”, Computer, Vol. 50, No. 5, pp. 36-43, 2017.
  • Luxchippiriya Balachandran and Abarnah Kirupananda, “Online Reviews Evaluation System for Higher Education Institution: An Aspect Based Sentiment Analysis Tool”, Proceedings of International Conference on Software, Knowledge, Information Management and Applications, pp. 1-7, 2017.
  • Arun Mukherjee, Bing Liu and Natalie Glance, “Spotting Fake Review Groups in Consumer Reviews”, Proceedings of 21st International Conference on World Wide Web, pp. 191-200, 2012.
  • Ioannis Dematis, Eirini Karapistoli and Athena Vakali, “Fake Review Detection via Exploitation of Spam Indicators and Reviewer Behavior Characteristics”, Proceedings of International Conference on Current Trends in Theory and Practice of Informatics, pp. 581-595, 2017.
  • Gang Liu, Wray Buntine, Weiping Fu and Yudan Du, “An Association Rules Text Mining Algorithm Fusion with K-Means Improvement”, Proceedings of International Conference on Computer Science and Network Technology, pp. 781-785, 2015.
  • Zhen Hai, Kuiyu Chang and Jung Jae Kim, “Implicit Feature Identification via Co-occurrence Association Rule Mining”, Proceedings of International Conference on Computational Linguistics and Intelligent Text Processing, pp. 393-404, 2011.
  • R. Barbado, O. Araque and C.A. Iglesias, “A Framework for Identifying Fake Review Detection in Online Consumer Electronics Retailers”, Information Processing and Management, Vol. 56, No. 4, pp. 1234-1244, 2019.
  • Neha S. Chowdhary and Anala A. Pandit, “Fake Review Detection using Classification”, International Journal of Computer Applications, Vol. 180, No. 50, pp. 975-987, 2018.
  • Xi Bin Jia, Ya Jin, Ning Li, Xing Su, Barry Cardiff and Bir Bhanu, “Words Alignment based on Association Rules for Cross Domain Sentiment Classification”, Frontiers of Information Technology and Electronic Engineering, Vol. 19, No. 2, pp. 260-272, 2018.
  • Swapna Gottipati, Venky Shankararaman and Jeff Rongsheng Lin, “Text Analytics Approach to Extract Course Improvement Suggestions from Students Feedback”, Research and Practice in Technology Enhanced Learning, Vol. 13, No. 6, pp. 1-19, 2018.
  • Rakibul Hassan and Md. Rabiul Islam, “Detection of Fake Online Reviews using Semi-Supervised and Supervised Learning”, Proceedings of International Conference on Electrical, Computer and Communication Engineering, pp. 1-5, 2019.
  • Jenifer Jothi Mary, S. Santiago and L. Arockiam, “A Methodological Framework to Identify the Students Opinion using Aspect based Sentiment Analysis”, International Journal of Engineering Research and Technology, Vol. 5, No. 2, pp. 642-645, 2018.
  • Vasileios Kagklis, Anthi Karatrantou, Maria Tantoula, Chris T. Panagiotakopoulos and Vassilios S. Verykios, “A Learning Analytics Methodology for Detecting Sentiment in Student: A Case Study in Distance Education”, European Journal of Open, Distance and E-Learning, Vol. 18, No. 2, pp. 74-94, 2015.
  • Xinyue Wang, Xianguo Zhang, Chengzhi Jiang and Haihang Liu, “Identification of Fake Reviews using Semantic and Behavioural Feature”, Proceedings of International Conference on Information Management, pp. 92-97, 2018.
  • K.C. Ravi Kumar, D. Teja Santosh and B. Vishnu Vardhan, “Extracting Opinion Targets from Product Reviews using Comprehensive Feature Extraction Model in Opinion Mining”, Indian Journal of Science and Technology, Vol. 10, No. 21, pp. 1-6, 2017.
  • Z. Kamisli Ozturk, Z.I Erzurum Cicek and Z. Ergul, “Sentiment Analysis: An Application to Anadolu University”, Proceedings of International Conference on Computational and Experimental Science and Engineering, pp. 752-755, 2017.

Abstract Views: 2

PDF Views: 0




  • Student Feedback Sentiment Analysis System for Distance Education using Arm with K-Means Clustering

Abstract Views: 2  |  PDF Views: 0

Authors

G. N. Harshini
Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, India
N. Gobi
Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, India

Abstract


The rapid development of Internet has resulted in the boom of evaluations about products and services. For extracting the aspects and determining the opinions from reviews Sentiment Analysis is used. The main challenges faced by Sentiment Analysis system is that, in order to increase or decrease the market value of the product the spammers may post irrelevant or fake reviews and another challenge deals with the classification of both Implicit and Explicit features present among the review sentences in the dataset. The proposed system deals with the identification of fake reviews through fake review Indicators which help in removing the fake reviews. For the better identification of both implicit and explicit features, association rule mining with K-Means Clustering is used. Lexicon method is used for the classification of sentiments into positive and negative polarities. The advantage of the proposed system is that the fake reviews can be detected and eliminated in the dataset and both implicit and explicit attribute extraction from the review sentence can be identified along with its polarities through Lexicon based Method.

Keywords


Lexicon Based Method, Sentiment Analysis, Spammers, K-Means Clustering.

References