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Sentiment analysis in MOOCs: a case study

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URI: https://hdl.handle.net/10016/32702
ISBN: 978-1-5386-2957-4
DOI: https://doi.org/10.1109/EDUCON.2018.8363409
UXXI: CC/0000028375
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sentiment_EDUCON_2018_ps.pdf (417.66 KB)
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2018-05-24
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IEEE
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Abstract
Forum messages in MOOCs (Massive Open Online Courses) are the most important source of information about the social interactions happening in these courses. Forum messages can be analyzed to detect patterns and learners' behaviors. Particularly, sentiment analysis (e.g., classification in positive and negative messages) can be used as a first step for identifying complex emotions, such as excitement, frustration or boredom. The aim of this work is to compare different machine learning algorithms for sentiment analysis, using a real case study to check how the results can provide information about learners' emotions or patterns in the MOOC. Both supervised and unsupervised (lexicon-based) algorithms were used for the sentiment analysis. The best approaches found were Random Forest and one lexicon based method, which used dictionaries of words. The analysis of the case study also showed an evolution of the positivity over time with the best moment at the beginning of the course and the worst near the deadlines of peer-review assessments.
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Proceeding of: 2018 IEEE Global Engineering Education Conference (EDUCON2018), 17-20 April, 2018, Santa Cruz de Tenerife, Canary Islands, Spain.
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Sentiment analysis, MOOCs, MOOC (Massive Online Open Course), Learners' behavior, Learning analytics, Machine learning
Bibliographic citation
Proceedings of 2018 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2018. Pp. 1489-1496.
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DIT - GAST - Comunicaciones en Congresos