Semantic Analysis to Identify Students’ Feedback

Abstract

Abstract In this research, an automated analysis is performed on students’ chat and text data generated by social media platforms over the course of one semester and thoroughly analyzed for potential feedback about teaching, exams, and course contents. A data crawler is developed that performs horizontal and vertical samplings of the data. After data crawling, a few preprocessing steps are performed including text extraction, noise removal, stop-word removal, word stemming, text classification, and feature extraction. The intensity of a review is determined using four measures containing knowledge and understanding, course contents, teaching style, and assessment procedures for a specific course. The proposed system contains features from text mining and web mining to automatically identify a review whenever a user writes comments on their studies. This system aims to provide curriculum development committees with valuable online student feedback and assist in curriculum improvements. By comparing these automated reviews to results obtained from manual student survey forms, we found that the automated system yields the same output but at a fraction of the cost and time typically spent on collecting and analyzing manual student surveys.