CFP last date
21 July 2025
Call for Paper
August Edition
IJCA solicits high quality original research papers for the upcoming August edition of the journal. The last date of research paper submission is 21 July 2025

Submit your paper
Know more
The week's pick
Responsive image
Navigating the Future of Cybersecurity: A Strategic Approach to Crypto Agility for Modern Enterprises

Aditya Gupta
Random Articles
Reseach Article

Sentiment Mining on Social Media using Naive Bayes: A Tool for Enhancing Academic Program Decisions

by Alphie P. Lavarias, Christian Ernes A Caranto, Junard S. Secretario, Queen Amchell B. Papa, Romulo L. Olalia Jr., Maynard Gel F. Carse
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 19
Year of Publication: 2025
Authors: Alphie P. Lavarias, Christian Ernes A Caranto, Junard S. Secretario, Queen Amchell B. Papa, Romulo L. Olalia Jr., Maynard Gel F. Carse
10.5120/ijca2025925301

Alphie P. Lavarias, Christian Ernes A Caranto, Junard S. Secretario, Queen Amchell B. Papa, Romulo L. Olalia Jr., Maynard Gel F. Carse . Sentiment Mining on Social Media using Naive Bayes: A Tool for Enhancing Academic Program Decisions. International Journal of Computer Applications. 187, 19 ( Jul 2025), 43-47. DOI=10.5120/ijca2025925301

@article{ 10.5120/ijca2025925301,
author = { Alphie P. Lavarias, Christian Ernes A Caranto, Junard S. Secretario, Queen Amchell B. Papa, Romulo L. Olalia Jr., Maynard Gel F. Carse },
title = { Sentiment Mining on Social Media using Naive Bayes: A Tool for Enhancing Academic Program Decisions },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2025 },
volume = { 187 },
number = { 19 },
month = { Jul },
year = { 2025 },
issn = { 0975-8887 },
pages = { 43-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number19/sentiment-mining-on-social-media-using-naive-bayes-a-tool-for-enhancing-academic-program-decisions/ },
doi = { 10.5120/ijca2025925301 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-07-09T01:07:44.309579+05:30
%A Alphie P. Lavarias
%A Christian Ernes A Caranto
%A Junard S. Secretario
%A Queen Amchell B. Papa
%A Romulo L. Olalia Jr.
%A Maynard Gel F. Carse
%T Sentiment Mining on Social Media using Naive Bayes: A Tool for Enhancing Academic Program Decisions
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 19
%P 43-47
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study investigates the application of sentiment analysis to social media posts related to academic programs, utilizing datasets composed of both Filipino and English texts. Employing a Naive Bayes classifier, the system achieved an overall classification accuracy of 78.66%, effectively distinguishing positive, negative, and neutral sentiments within the feedback. The data preprocessing pipeline included thorough cleaning, normalization, tokenization, stopword removal, and lemmatization, all of which contributed to enhanced model performance. These findings demonstrate the practical utility of sentiment analysis as an analytical tool for academic institutions seeking to gauge stakeholder opinions and feedback. By identifying trends in sentiment, educational administrators can make informed decisions to improve program quality and engagement.

References
  1. Aqlan, A., Bairam, M., & Naik, R. L. (2019). A Study of Sentiment Analysis: Concepts, Techniques, and Challenges. (pp. 147-162). DOI: 10.1007/978-981-13-6459-4_16.
  2. B. Dhiman, "Ethical Issues and Challenges in Social Media: A Current Scenario: 5 Apr 2023, J.C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, India, March 29, 2023
  3. Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., & Gavaldà, R. (2012). MOA: Massive Online Analysis. The Journal of Machine Learning Research, 11, 1601–1604. CBM.
  4. Camilleri, M.A. (2019). Higher Education Marketing: Opportunities and Challenges in the Digital Era. Academia, 0(16-17), 4-28. DOI:10.26220/aca.3169Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  5. Damota, M. D. (2019). The Effect of Social Media on Society. New Media and Mass Communication, Vol. 78. Retrieved from www.iiste.org ISSN 2224-3267 (Paper) ISSN 2224-3275 (Online) DOI: 10.7176/NMMC
  6. Farhan Aftab, Sibghat Ullah Bazai, Shah Marjan, & Laila Baloch (2023). A Comprehensive Survey on Sentiment Analysis Techniques. International Journal of Technology, 4(6), 1288. DOI: 10.14716
  7. Gurusamy, V., & Kannan, S. (2014). Preprocessing Techniques for Text Mining. The Journal of Machine Learning Research, 11, 1601–1604. Retrieved from Research Gate.
  8. Iglesias, C. A., & Moreno, A. (2019). Sentiment Analysis for Social Media. Applied Sciences, 9(23), 5037. DOI: 10.3390/app9235037.
  9. Kadhim, A. (2018). An Evaluation of Preprocessing Techniques for Text Classification. International Journal of Computer Science and Information Security, 16(6), 22-32. ResearchGate
  10. Lasi, M.b.A., Hamid, A.B.b.A., Jantan, A.H.b., Goyal, S.B., Tarmidzi, N.N.b. (2024). Improving Digital Marketing Using Sentiment Analysis with Deep LSTM. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 785. Springer, Singapore
  11. Petrosyan, A. (2024, May 7). Worldwide digital population 2024. Statista. [https://www.statista.com/statistics/617136/digita l-population-worldwide/]
  12. Rajesh, M. A., & Hiwarkar, T. (2023). Exploring Preprocessing Techniques for Natural Language Text: A Comprehensive Study Using Python Code. International Journal of Engineering Technology and Management Sciences, 7(5), DOI:10.46647/ijetms.2023.v07i05.047. Semantic Scholar.
  13. Ramasubramanian, K., Singh, A. (2019). Machine Learning Model Evaluation. In: Machine Learning Using R. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4215- 5_7
  14. Šušter, I., & Ranisavljević, T. (2023). Optimization of MySQL database. Journal of Process Management and New Technologies, DOI:10.5937/jouproman2301141qCorpus ID: 259754662.Semantic Scholar.
  15. Taherdoost, H. (2021). Data Collection Methods and Tools for Research; A Step-by-Step Guide to Choose Data Collection Technique for Academic
  16. Tan KL, Lee CP, Lim KM. A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research. Applied Sciences. 2023; 13(7):4550.
  17. Journal of Innovative Research in Applied Sciences and Engineering, 4(4), 2456-8910. DOI: 10.29027/IJIRASE.v4.i4.2020.735-742.
  18. Uçar, U., Nour, M. K., Sindi, H. F., & Polat, K. (2020). The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets. *Mathematical Problems in Engineering, 2020*(1), 1-17. DOI: 10.1155/2020/2836236
  19. Vadlamudi, P. S., Gunasekaran, M., Nagalakshmi, T. J., & Saveetha University. (2023). An Analysis of the Effectiveness of the Naive Bayes Algorithm and the Support Vector Machine for Detecting Fake News on Social Media. In 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) (pp. DOI:10.5937/jouproman2301141qCorpus ID: 259754662)
  20. Vyas, K. (2023). 8 Major Advantages of Using MySQL. DataMation.
Index Terms

Computer Science
Information Sciences
Pattern Recognition
Data Mining
Educational Technology
Information Retrieval

Keywords

Artificial Intelligence Academic Program Educational Institution Natural Language Processing Machine Learning Algorithm Sentiment Analysis Stemming Tokenization

Powered by PhDFocusTM