An Efficient Classification of Emotions in Students' Feedback using Deep Neural Network
Source of Publication
2022 13th International Conference on Information and Communication Systems, ICICS 2022
Background and Objective: In both the corporate and academic worlds, the collection and analysis of feedback (product evaluation, social media debate, and student input) has long been a significant topic. The traditional approaches to collect student feedback focused on data collection and analysis via questionnaires. However, the student makes comments on social media sites that need to be looked at to improve educational standards at schools.Methods: The purpose of this work is to construct a deep neural network-based system to assess students' feedback and emotions found in the reviews. Our approach applies a Deep Learning-based Bi-LSTM Model to a benchmark student input dataset. It would categorize students' feedback about their instructors according to their emotional states, such as love, happiness, fury, and disdain.Results: The experimental findings demonstrate that the proposed approach outperforms both benchmark studies and state-of-the-art machine learning classifiers.
Deep learning, Social networking (online), Communication systems, Neural networks, Benchmark testing, Data collection, Standards
Asghar, Muhammad Zubair; Masood Khattak, Asad; Khan, Nouman; Alam, Muhammad Mansoor; Lajis, Adidah; Rahmat, Mohd Khairil; and Mohamad Nasir, Haidawati, "An Efficient Classification of Emotions in Students' Feedback using Deep Neural Network" (2022). All Works. 5243.
Indexed in Scopus