Fuzzy rule based sentiment analysis for finding University Student Satisfaction in Yemen

Siham Abdulmalik Mohammed Almasani; Wadeea Ahmed Abdo Qaid; Jamil Abdulhamid Mohammed Saif; Ibrahim A A Alqubati

doi:10.17485/IJST/v14i44.1283

Article

Fuzzy rule based sentiment analysis for finding University Student Satisfaction in Yemen

VIEWS 1510
PDF 210

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i44.1283

Year: 2021, Volume: 14, Issue: 44, Pages: 3264-3269

Original Article

Fuzzy rule based sentiment analysis for finding University Student Satisfaction in Yemen

Siham Abdulmalik Mohammed Almasani^1*, Wadeea Ahmed Abdo Qaid²,¹, Jamil Abdulhamid Mohammed Saif³, Ibrahim A A Alqubati⁴

¹Assistant professor, Faculty of Computer Science and Engineering, Hodeidah University,Yemen
²Dean Faculty of Engineering and Technology, Future University, Yemen
³Department of Computer and Information Systems, College of Community, University of
Bisha, KSA
⁴Computer Department, Community College, Najran University, KSA

*Corresponding Author
Email: [email protected]

Received Date:21 July 2021, Accepted Date:20 November 2021, Published Date:24 December 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To analyze the sentiment and understand Student’s opinions and satisfaction about university, and the services which introduce by it to reach accuracy in evaluation of the university. Methods: The sentiment of students was analyzed and quantified the satisfaction towards the University by building an algorithm using a set of fuzzy rules. This study proposes an integrated optimization method: applying a set of fuzzy rules with two lexicons (SentiWordNet and AFINN lexicon) besides to other parameter. The lexicons in sentiment anlysis are individual words that can be considered as a unit of opinion information. The proposed fuzzy system integrates Natural Language Processing techniques to analyze and quantify student’s satisfaction towards the University by classify the comments into very positive, positive, very negative, negative, or neutral sentiment classes. Findings: with this approach, the accuracy is more 0.891 compared to Support Vector Machines (SVM), Naïve Bayes, The Fuzzy Inference to analyze the sentiment with their own lexicon Opinion Words Lexicon and classified the sentiment into 2 classpositive, negative and The Fuzzy Inference to analyze the sentiment with only SentiWordNet lexiconin evaluation obtained for University evaluation, the evaluation has been verified by simulation results on MATLAB. Novelty: The novelty of this study lies in the formulation of few fuzzy rules to evaluate the sentiment class of tweets, and the proposed model can be adapted to any lexicon.

Keywords: Sentiment analysis; Fuzzy rule; linguistic variables; Students comments analysis

References

Birjali M, Kasri M, Beni-Hssane A. A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems. 2021;226:107134. Available from: https://dx.doi.org/10.1016/j.knosys.2021.107134
Kastrati Z, Dalipi F, Imran AS, Nuci KP, Wani MA. Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study. Applied Sciences. 2021;11(9):3986. Available from: https://dx.doi.org/10.3390/app11093986
Ho IMK, Cheong KY, Weldon A. Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques. PLOS ONE. 2021;16(4):e0249423. Available from: https://dx.doi.org/10.1371/journal.pone.0249423
Parveen H, Pandey S. Sentiment analysis on Twitter Data-set using Naive Bayes algorithm. 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). 2016;p. 416–419. doi: 10.1109/ICATCCT.2016.7912034
Hamdan H, Hussam, Béchet, Frederic, Bellot, Patrice. Experiments with DBpedia, WordNet and SentiWordNet as resources for sentiment analysis in micro-blogging. Second Joint Conference on Lexical and Computational Semantics (SEM). 2013;2:455–459. Available from: https://aclanthology.org/S13-2075.pdf
Windasari IP, Uzzi FN, Satoto KI. Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek. 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). 2017;p. 266–269. Available from: 10.1109/ICITACEE.2017.8257715
Esparza GG, De-Luna A, Zezzatti AO, Hernandez A, Ponce J, Álvarez M, et al. A Sentiment Analysis Model to Analyze Students Reviews of Teacher Performance Using Support Vector Machines. International Symposium on Distributed Computing and Artificial Intelligence, Springer, Cham. 2018;p. 157–164. Available from: https://link.springer.com/chapter/10.1007/978-3-319-62410-5_19
Srivastava R, Bhatia MPS. Quantifying modified opinion strength: A fuzzy inference system for Sentiment Analysis. 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2013;p. 1512–1519. doi: 10.1109/ICACCI.2013.6637404
Haque A, Rahman T. Sentiment Analysis by Using Fuzzy Logic. International Journal of Computer Science, Engineering and Information Technology. 2014;4(1):33–48. Available from: https://dx.doi.org/10.5121/ijcseit.2014.4104
Zadeh LA. The Concept of a Linguistic Variable and its Application to Approximate Reasoning. Information Science. 1974;8:199–249. Available from: https://www.sciencedirect.com/science/article/abs/pii/0020025575900365
Zadeh LA. Fuzzy logic—a personal perspective. Fuzzy Sets and Systems. 2015;281:4–20. Available from: https://dx.doi.org/10.1016/j.fss.2015.05.009
Stefano B, EA, Fabrizio S. Sentiwordnet 3.0: An en- hanced lexical resource for sentiment analysis and opinion mining. European Language Resources Association. 2010;10:2200–2204. Available from: https://aclanthology.org/L10-1531/
Ramshankar N, Prathap PMJ. A novel recommendation system enabled by adaptive fuzzy aided sentiment classification for E-commerce sector using black hole-based grey wolf optimization. Sādhanā. 2021;46(3):1–24. Available from: https://dx.doi.org/10.1007/s12046-021-01631-2
Ghiassi M, Skinner J, Zimbra D. Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications. 2013;40(16):6266–6282. Available from: https://dx.doi.org/10.1016/j.eswa.2013.05.057
Almasani SAM, Finaev VI, Qaid WAA, Tychinsky AV. The Decision-making Model for the Stock Market under Uncertainty. International Journal of Electrical and Computer Engineering (IJECE). 2017;7(5):2782. Available from: https://dx.doi.org/10.11591/ijece.v7i5.pp2782-2790
Almasani SAM, Finaev VI, qaid WAa, tychinsky Av. Assessing the Current State of the Stock Market Under Uncertainty. Journal of Theoretical and Applied Information Technology. 2016;89(1). Available from: http://iues.sfedu.ru/wp-content/uploads/2016/08/Almasani-skopus2_2016.pdf

Copyright

© 2021 Almasani et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)