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Optimal e-learning course recommendation with sentiment analysis using hybrid similarity framework

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Abstract

Nowadays, online course recommendation frameworks are popular for suggesting courses to new learners. Various courses are available on the Internet, and it is very challenging to suggest optimal learning courses for learners. This work presented an effective online course recommendation with sentiment analysis using hybrid similarity based approaches. Initially, the input text online course data is pre-processed using tokenization, stop word removal, Stemming, noise filtering, and lemmatization processes. Then, effective features like Lexicon combined TF-IDF feature, Word2vector features, N-gram feature, and Glove feature are extracted from the pre-processed data. Subsequently, the improved horse herd Optimization (IHHO) algorithm is utilized to select features optimally. Afterwards, a hybrid Bidirectional long short term memory-convolutional neural network (Hybrid BLSTM-CNN) is presented to classify courses into positive, negative and neutral for the sentiment analysis of courses. Here, the adaptive salp swarm emperor penguin optimization (ASEP) approach is utilized to update the optimized weights of the hybrid classifiers. Finally, the hybrid content-collaborative similarity (Hybrid-CCS) approach is considered for an optimal course recommendation. The experimental results of the developed approach outperformed the compared existing schemes in terms of accuracy (99.79%), F1-score (99.648%), Kappa (99.061%), RMSE (0.2092), precision (99.69%), recall (99.698%), MAE (0.012), HR (0.9321) and ARHR (0.1621) and AUC (99.79%).

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Authors and Affiliations

  1. SCSE, VIT Bhopal University, Bhopal, India

    Roshan Sureshrao Bhanuse & Sandip Mal

  2. Yeshwantrao Chavan College of Engineering, Wanadongri, Nagpur, India

    Roshan Sureshrao Bhanuse

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  1. Roshan Sureshrao Bhanuse
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  2. Sandip Mal
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Correspondence to Roshan Sureshrao Bhanuse.

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Bhanuse, R.S., Mal, S. Optimal e-learning course recommendation with sentiment analysis using hybrid similarity framework. Multimed Tools Appl 83, 16417–16446 (2024). https://doi.org/10.1007/s11042-023-16138-7

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