Students’ Sentiment Analysis Using Natural Language Toolkit in Machine Learning for Module Evaluation

Carine Umunyana, Gerard Tuyizere and Anaclet Mbarushimana
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Carine Umunyana: Ministry of Environment, Rwanda
Gerard Tuyizere: Integrated Polytechnic Regional College (RP/IPRC Ngoma), Rwanda
Anaclet Mbarushimana: Integrated Polytechnic Regional College (RP/IPRC Ngoma), Rwanda

European Journal of Engineering and Technology Research, 2024, vol. 9, issue 1, 72-75

Abstract: This paper presents a combination of natural language toolkit (NLTK) in machine learning for sentiment analysis used for module evaluation. The module evaluation is typically done at the end of each module. Dataset of 300 students evaluating each module is conducted with excellent, very good, good, fair, and poor sentiments, delivers valuable perceptions into the overall teaching and lecturing quality and decision making for enlightening methodology of teaching and approaches. This paper demonstrates sentiment analysis model trained using logistic regression algorithm in Machine Learning to evaluate the sentiments given by students in their module evaluation. A study comparison has been done between the proposed model and other sentiment analysis for module evaluation. The results of experiments have been analyzed for decision-making.

Keywords: Machine learning and module evaluation; natural language toolkit; sentiment analysis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:9:y:2024:i:1:id:63006

DOI: 10.24018/ejeng.2024.9.1.3006

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