Augmented language model with deep learning adaptation on sentiment analysis for E-learning recommendation |
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| Affiliation: | 1. Department of Computer Science and Engineering, C.V. Raman Global University, Bidya Nagar, Mahura, Janla, Bhubaneswar, Odisha 752054, India;2. Department of Computer Science and Information Technology, C.V. Raman Global University, Bidya Nagar, Mahura, Janla, Bhubaneswar, Odisha 752054, India;4. Department Computer Science and Engineering, University of Bridgeport, Bridgeport, CT, USA;1. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India;2. Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur 713209, West Bengal, India;3. Department of Computer Science and Engineering, Institute of Engineering & Technology, GLA University, Mathura 281406, Uttar Pradesh, India;4. Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, West Bengal, India;1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran;2. Department of Energy Engineering and Physics, Medical Radiation Engineering Group, Amirkabir University of Technology, Tehran, Iran;1. University of Western, Australia;2. Defence Science and Technology Group, Australia;3. Ergonomie, Australia;1. Electrical and Electronics Engineering Department, Zonguldak Bülent Ecevit University, Zonguldak 67100, Turkey;2. Electrical and Electronics Engineering Department, Istanbul Medeniyet University, Istanbul 34700, Turkey |
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| 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 models based 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-CNN model outperformed other models with an accuracy of 90.37 % for fine-grained sentiment classification on 5 discrete classes and the empirical results are compared. |
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| Keywords: | E-learning adaptation Recommender system Sentiment analysis Convolutional neural network Natural language processing Word embeddings |
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