Vol.15, No.2, May 2026.                                                                                                                                                                          ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

A Sentiment Analysis Model of the Library Services: Prince of Songkla University on Facebook Opinions Using Naive Bayes Classifier

Nawapon Kaewsuwan, Wannisa Matcha, Siriwan Kajornkasirat, Komgrit Rumdon

© 2026 Nawapon Kaewsuwan, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 15, Issue 2, Pages 1892-1901, ISSN 2217-8309, DOI: 10.18421/TEM152-78, May 2026.

Received: 23 April 2025.
Revised: 27 October 2025.
Accepted: 12 February 2026.
Published: 27 May 2026.

Abstract:

Facebook is an alternative tool that can reflect individuals’ opinions, attitudes, and perceptions about the quality of products and services. It is also a channel for reflecting opinions or making demands that lead to quality improvement or development of products and services. Furthermore, Facebook serves as a platform that facilitates active personal engagement within online communities, enabling the sharing of knowledge and the exchange of experiences and opinions regarding the quality of products or services over a specified period. Most studies on sentiment analysis on Facebook focus on analyzing English text only, there is a derth in sentiment analysis using Thai language, especially relating to library users’ experiences. Therefore, this study focused on analysis by calling positive and negative opinions of Thai library users using the Naive Bayes classifier. The results revealed that heavy negative comments were posted in Library Facebook pages. The detailed exploration on the keywords found that most of the users expressed their negative experiences on facilities and services of the library. Whereas, the positive comments were found to be related to the facilities of the library.

Keywords – Sentiment analysis, library, Facebook, classification, Naive Bayes classifier.

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