Student Sentiment Analysis with Academic Performanceand job ‎Placement Using Attention Based Deep Recurrent Learning

  • Authors

    • S Johnbosco Department of Computer Science, Sacred Heart College (Autonomous), Affiliated to Thiruvalluvar University, Tirupattur (Dt), Tamil ‎Nadu, India
    • Dr. Ravi Lourdusamy Department of Computer Science, Sacred Heart College (Autonomous), Affiliated to Thiruvalluvar University, Tirupattur (Dt), Tamil ‎Nadu, India
    • Dr. Denis R Department of Computer Science, Mount Carmel College, Autonomous, Bengaluru, Karnataka, India
    • Dr. Peter Jose P Department of Computer Science, Mount Carmel College, Autonomous, Bengaluru, Karnataka, India
    • Dr. Mithun D'Souza Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, India
    • Dr. Prabakaran T Department of Artificial Intelligence and Machine Learning, Sacred Heart College (Autonomous), Tirupattur (Dt, Tamil Nadu, India
    • Dr. P. Sathish Department of Master of Computer Applications, Nitte Meenakshi Institute of Technology, Nitte (Deemed to be University), Bangalore, India
    https://doi.org/10.14419/7p5x8077

    Received date: September 21, 2025

    Accepted date: November 7, 2025

    Published date: December 16, 2025

  • Deep Learning; Ridge Regularization; Bidirectional Gated Recurrent Neural Network; Round Robin, Attention

  • Abstract

    Technological evolution has significantly innovated education, with student feedback sentiment analysis functions to assess the educational ‎quality achieved. Amid numerous teaching and learning processes, educational institutions utilize technology to collect information regarding student experiences and assess their teaching methods. Educational institutions should provide a healthy learning environment and facilitate a successful teaching and learning process. The level of satisfaction through placement performance reflects a clear understanding of the ‎university environment and the services provided to students. For most current sentiment analysis of students' feedback methods, capturing ‎the complex semantic features is laborious and cumbersome, and these features are not entirely relevant to the analysis of student sentiments. ‎This work proposes a novel sentiment analysis of students' feedback on academic and placement performance using Deep Learning (DL), ‎specifically the Ridge-Regularized Bidirectional Gated Recurrent Attention-Based Classifier (RR-BGRAC). First, with the raw samples obtained from the student placement dataset serving as a base for student feedback, Ridge Class-balanced Bidirectional Gated Recurrent Attention-based Round-robin Feature engineering is applied. The feature engineering in our work involves reducing dimensionality by selecting the most relevant features for performing semantic analysis in order to ascertain the placement. With class-balanced samples and the most relevant ‎features as the basis, the Softmax Classifier is applied to generate a suggested job role based on academic strengths. Based on the student ‎placement data and focusing on the student feedback sentiments, the proposed DL-based RR-BGRAC method is experimentally demonstrated. The results show that the accuracy rate and recall rate of student sentiment analysis are improved by 15% and 18%, respectively, and training time and space are minimized by 19% and 16%, compared to traditional methods‎.

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  • How to Cite

    Johnbosco, S., Lourdusamy, D. R. ., R, D. D. ., P, D. P. J. ., D'Souza, D. M. ., T, D. P. ., & Sathish, D. P. . (2025). Student Sentiment Analysis with Academic Performanceand job ‎Placement Using Attention Based Deep Recurrent Learning. International Journal of Basic and Applied Sciences, 14(8), 322-339. https://doi.org/10.14419/7p5x8077