Abstract
Sentiment analysis (SA) is one of the methods that can assist in extracting information from a large amount of data. It is considered one of the research fields in text mining, which has become vital to employ within recommendation systems, as well as in e-learning environments. In the current work, we present a new method of recommendation model utilizing sentiment analysis based on convolutional neural network (SABCNN) and natural language processing (NLP) techniques. Starting from collecting and analyzing the learners’ sentiments of reviews for the e-content with their corresponding rating within e-platforms, a sentence or a specific text is classified to multi-levels by determining what semantics of feelings it holds. Our research aims towards recommending learning resources that are relevant to the learners’ preferences with the aid of the previous reviews of other learners, sharing him/her the top preferences.
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Alatrash, R., Ezaldeen, H., Misra, R., Priyadarshini, R. (2021). Sentiment Analysis Using Deep Learning for Recommendation in E-Learning Domain. In: Panigrahi, C.R., Pati, B., Pattanayak, B.K., Amic, S., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1299. Springer, Singapore. https://doi.org/10.1007/978-981-33-4299-6_10
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DOI: https://doi.org/10.1007/978-981-33-4299-6_10
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