Analyzing End-of-Course Comments by Using Deep Hierachical Net

The extensive feedback that students left at the end of courses can be very helpful in enhancing and honing the instruction. The comments, expressed in text format, cannot be effectively represented in a way that the computer can recognize, and the meaning implied in the comments is always blurry, making it more difficult for the computer to understand them. As a result, it is challenging for the computer to use these comments and comprehend student appeal. In this paper, we address the representation problem by projecting comments (including words) into a dense vector space. We suggest using a highly deep neural network to capture the textual meaning while examining a real-world dataset of five-year comments from end-of-course evaluations at a university. The suggested model is assessed based on its capacity to identify the sentimental inclinations of these remarks, yielding insightful recommendations and critiques.