Abstract
In the academic area, teaching process assessment conducted by students can be used as the main information to improve the teaching and learning process. However, when examination or consideration of the student feedback is conducted by teachers, the outcome may be a biased analysis. In the last decade, sentiment analysis has been applied to automatically evaluate the teaching process because it may help to reduce the problem of biased analysis when the sentiment analysis is performed by humans. This work presents a method of automatically analyzing student feedback relevant to teaching process assessment. The proposed method is called sentence-level sentiment analysis, and it is driven by processing steps such as pre-processing student comments and text representation, identifying aspect class for each sentence using the aspect analyzer, assigning sentence polarity for each sentence using the sentiment analyzer, and summarizing the overall sentiment polarity by considering student comments, respectively. The proposed method returns the recall, precision, F1, and accuracy scores of 0.835, 0.825, 0.825, and 0.825, respectively. These were satisfactory results.
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Chantamuang, O., Polpinij, J., Vorakitphan, V., Luaphol, B. (2022). Sentence-Level Sentiment Analysis for Student Feedback Relevant to Teaching Process Assessment. In: Surinta, O., Kam Fung Yuen, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2022. Lecture Notes in Computer Science(), vol 13651. Springer, Cham. https://doi.org/10.1007/978-3-031-20992-5_14
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