Attention-aware with stacked embedding for sentiment analysis of student feedback through deep learning techniques

Author:

Malik Shanza Zafar1,Iqbal Khalid1,Sharif Muhammad1,Shah Yaser Ali1,Khalil Amaad2,Irfan M. Abeer2,Rosak-Szyrocka Joanna3

Affiliation:

1. Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, Punjab, Pakistan

2. Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar, KPK, Pakistan

3. Faculty of Management, Czestochowa University of Technology, Częstochowa, Poland

Abstract

Automatic polarity prediction is a challenging assessment issue. Even though polarity assessment is a critical topic with many existing applications, it is probably not an easy challenge and faces several difficulties in natural language processing (NLP). Public polling data can give useful information, and polarity assessment or classification of comments on Twitter and Facebook may be an effective approach for gaining a better understanding of user sentiments. Text embedding techniques and models related to the artificial intelligence field and sub-fields with differing and almost accurate parameters are among the approaches available for assessing student comments. Existing state-of-the-art methodologies for sentiment analysis to analyze student responses were discussed in this study endeavor. An innovative hybrid model is proposed that uses ensemble learning-based text embedding, a multi-head attention mechanism, and a combination of deep learning classifiers. The proposed model outperforms the existing state-of-the-art deep learning-based techniques. The proposed model achieves 95% accuracy, 97% recall, having a precision of 95% with an F1-score of 96% demonstrating its effectiveness in sentiment analysis of student feedback.

Publisher

PeerJ

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