Data Collection Tools in Faculty Evaluation Sentiment Analysis

The rapid shift to online learning during this period highlighted the need for new feedback mechanisms, leading institutions to depend heavily on digital platforms, such as online surveys. This research utilizes a two-phase methodology to provide a comprehensive understanding of the changes in sentiment over time. The first phase involves a comparative matrix study of $\mathbf{8 5}$ relevant papers, analyzing key dimensions like data collection tools, platforms, algorithms, and the objectives of sentiment analysis in education. The second phase involves collecting both quantitative and qualitative data from faculty evaluations spanning the 2021-2022 pandemic period to the 2024-2025 post-pandemic period, across different academic levels. This mixed-methods approach helps trace sentiment trends over time. Key findings reveal the growing importance of sentiment analysis in education, particularly in evaluating teacher performance, automating feedback processes, and supporting professional development. A notable trend emerged during the transition from remote to in-person learning (2023-2025). The 2023-2024 semester saw the highest levels of positive ($\mathbf{2, 6 6 8}$ responses) and neutral ($\mathbf{3 4 4}$ responses) sentiment, indicating increased student engagement and satisfaction in face-to-face environments. However, by 20242025, there was a decline in all sentiment categories, especially in positive responses (596), which may suggest shifting student expectations or difficulties adjusting to in-person learning. These insights underline the complex relationship between learning modalities and student satisfaction. While face-to-face learning generally fosters higher engagement, maintaining this positive sentiment requires continuous faculty reflection and course improvements. This study provides valuable guidance for institutions striving to enhance the student experience in a post-pandemic educational landscape.