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All Journal International Journal of Multidisciplinary: Applied Business and Education Research
Rowell Santos
Baliuag University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Environmental Science Medicine & Pharmacology

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Faculty Evaluation Decision Based System with Sentiments Analysis using Naïve Bayes Algorithm Rowell Santos; Ana Lizel Del Poso; Clarissa Vicente
International Journal of Multidisciplinary: Applied Business and Education Research Vol. 7 No. 5 (2026): International Journal of Multidisciplinary: Applied Business and Education Rese
Publisher : Future Science / FSH-PH Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/ijmaber.07.05.18

Abstract

In the educational setting, the core element of education is the quality of instruction. This is truly evident as instruction has been one separate area in terms of educational and program accreditations. The main catalyst of instruction is the performance of teachers in terms of providing quality education. To evaluate faculty members, most academic institutions are using a standard instrument. The evaluation questionnaire consists of quantitative and qualitative questions. The quantitative question is typically answered using the Likert scale model. However, open feedback, typically, is not included in the performance evaluation or appraisal due to a lack of automated text analytics methods. The creation of a Faculty Evaluation Decision Based System with Sentiments Analysis using Naïve Bayes Algorithm provides a more comprehensive understanding of the teacher’s evaluation ratings. Evolutionary Prototyping Model was used as a software methodology which provides systematic and controlled procedures for building iterative prototypes. The model consists of four phases, which include, identification, design, construction or building, and evaluation. The overall rating of the respondents using the ISO/IEC 25010, or the Software Product Quality Model Criteria is 4.82 numerical rating with an interpretation of very acceptable. As observed all criteria are rated very acceptable which indicates a high standard has been set in the development of the system. The Naïve Bayes algorithm successfully categorized comments into negative and positive classifications. Mapping Analysis based on the evaluation results of the system was successfully embedded into the System.
Co-Authors Ana Lizel Del Poso Clarissa Vicente