Smart Faculty Evaluation: A Mobile App Using NLP-Based Sentiment Analysis and Random Forest for Faculty Assessment at Universidad De Manila

Faculty evaluation is an essential tool of teaching competence assurance and institutional quality progression. Nevertheless, a significant number of academic institutions still use traditional approaches which are repetitive, subjective and narrow in scale. The current study focuses on creating the Smart Faculty Evaluation: A Mobile Application with NLP-based Sentiment Analysis and Random Forest, which is designed specifically to be used in the Universidad de Manila. The system incorporates Natural Language Processing (NLP) to automatically analyze open-ended student feedback in a systematic way and convert qualitative feedback into formatted, evidence-based insights. Meanwhile, the Random Forest method is used to enhance the precision and consistency of the classification and measure the performance of the faculty. The project was developed based on Agile development principles, and with the assistance of these principles, it was designed, tested, and refined in an iterative manner to be responsive to the needs of users and the requirements of the institution. The system was tested in terms of ISO/IEC 25010 software quality standards. The findings indicated overall weighted mean scores of between 4.46 and 4.53, which can be seen as Above average or Excellent in terms of functional suitability, performance efficiency, reliability, security, portability and usability. These results prove that the suggested app will be effective, reliable, and convenient to operate. On the whole, the Smart Faculty Evaluation App is an automated and formatted approach to the evaluation of faculty which increases the levels of transparency, accessibility and efficacy in the evaluation of faculty.