Sentiment Analysis and Issue Classification in Teacher Professional Training for Pre-Service Teachers at Rattanakosin Rajabhat University
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Abstract
This study aims to analyze sentiment towards teacher training and classify associated issues using supervised machine learning. The research methodology comprised four main stages: data collection and preparation, data processing, modeling, and model evaluation. Data were gathered via Google Form from 466
pre-service teachers at Rattanakosin Rajabhat University who participated in teacher training during the 2023 academic year. After filtering and randomizing, 784 messages (80%) were allocated for training data, while 196 messages (20%) were reserved for testing. Supervised learning techniques were employed to develop models for sentiment analysis and issue classification. The results indicated high accuracy for both the sentiment analysis model (0.94) and the issue classification model (0.88), demonstrating effective prediction capability. Of the 196 analyzed messages, sentiment analysis revealed 110 positive sentiment messages (56.35%) and 86 negative sentiment messages (43.65%). Following classification, nine distinct issues were identified, with notable categories including teaching (97 messages, 49.49%), student-related concerns (35 messages, 17.86%), and miscellaneous issues related to school (1 message, 0.51%). The results can be used to better prepare pre-service teachers in various aspects, particularly in addressing issues related to students, which were identified as the most negatively perceived aspect, ensuring more effective teacher training experiences.
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