Augmented language model with deep learning adaptation on sentiment analysis for E-learning recommendation

Abstract

Sentiment Analysis is considered as an important research field in text mining, and is significant in recommendation systems and e-learning environments. This research proposes a new methodology of e-learning hybrid Recommendation System Based on Sentiment Analysis (RSBSA) by leveraging tailored Natural Language Processing (NLP) and Convolutional Neural Network (CNN) techniques, to recommend appropriate e-learning materials based on learner’s preferences. Integration is done on fine-grained sentiment analysis models, to classify text reviews of e-content posted on e-learning platform. Two enhanced language models based on ‘Continuous Bag of Word’ and ‘Skip-Gram’ are introduced. Moreover, three resilient language modelsbased on the hybrid language techniques are developed to produce a superior vocabulary representation. These models were trained using various CNN models to predict ratings of resources from online reviews provided by learners. To accomplish this, a customizable dataset ‘ABHR-1′ is used, which is derived from e-content' reviews with corresponding ratings labeled [1–5]. The proposed models are evaluated and tested using ABHR-1 and two public datasets. According to the simulation results, Multiplication-Several-Channels-CNNmodel outperformed other models with an accuracy of 90.37 % for fine-grained sentiment classification on 5 discrete classes and the empirical results are compared.