Automatic Sentiment Annotation Using Grok AI for Opinion Mining in a University Learning Management System - Julianto, Indri Tri; Sidqi, Muhammad Affan Al (Journal of Intelligent Systems Technology and Informatics, 2025)
Sentiment analysis has become an essential tool in evaluating user feedback on digital learning platforms. Understanding student sentiments toward Learning Management Systems (LMS) in higher education can offer critical insights for system development and service improvement. This study aims to evaluate the effectiveness of AI-assisted sentiment labeling using Grok AI and ChatGPT compared to manual labeling for sentiment classification of student opinions on LMS at Institut Teknologi Garut. The research involved distributing an online questionnaire to 96 students across four academic levels, collecting open-ended responses regarding their LMS usage experiences. These responses were preprocessed through case folding, cleaning, tokenization, stopword removal, and stemming. The sentiment labels were assigned using Grok AI, ChatGPT, and manual annotation, and the resulting datasets were used to build classification models using the Naïve Bayes algorithm in Altair RapidMiner with 10-Fold Cross Validation. The performance evaluation shows that manual labeling yielded the highest accuracy (52.22%) and Cohen's Kappa (0.137), followed by ChatGPT (50.11%, 0.119) and Grok AI (48.00%, 0.087). Word cloud visualizations further revealed the dominant themes within each sentiment class, indicating that positive opinions emphasized helpfulness and ease of use, while negative ones focused on access issues and system lags. This research suggests that AI-assisted labeling methods can be viable alternatives, although manual labeling still offers slightly better accuracy.