Book Chapter or Conference Paper Title
Sentiment analysis of student feedback using machine learning and lexicon based approaches
Faculty / School
Faculty of Computer Sciences (FCS)
Department of Computer Science
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2017 International Conference on Research and Innovation in Information Systems (ICRIIS)
16-17 July 2017
Institute of Electrical and Electronics Engineers (IEEE)
Abstract / Description
This paper presents a combination of machine learning and lexicon-based approaches for sentiment analysis of students feedback. The textual feedback, typically collected towards the end of a semester, provides useful insights into the overall teaching quality and suggests valuable ways for improving teaching methodology. The paper describes a sentiment analysis model trained using TF-IDF and lexicon-based features to analyze the sentiments expressed by students in their textual feedback. A comparative analysis is also conducted between the proposed model and other methods of sentiment analysis. The experimental results suggest that the proposed model performs better than other methods.
Nasim, Z., Rajput, Q., & Haider, S. (2017). Sentiment analysis of student feedback using machine learning and lexicon based approaches. (2324-8157), 1-6. https://doi.org/10.1109/ICRIIS.2017.8002475