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Enhancing faculty evaluation through NLP-based sentiment analysis of student feedback

Year: 2025

Type: article

Abstract: Faculty performance evaluation is crucial for maintaining high teaching standards in academic institutions. Traditional feedback mechanisms often rely on numerical ratings or manually reviewed responses, which lack depth in capturing student sentiments. This paper presents an NLP-powered sentiment analysis system that extracts meaningful insights from textual student feedback. The system classifies sentiments to highlight faculty strengths and areas for improvement. The analysis results are visualized through interactive dashboards, enabling faculty to track their performance trends and administrators to manage faculty data efficiently. By automating sentiment analysis and integrating data-driven feedback loops, this system fosters continuous faculty development and enhances the overall academic environment.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17053823

Authors: Kale, Nagavenkata Srinivas, Nakka, Seetaramalakshmi, Chowdari, Hemanth, Voddepally, Saikumar, Gadam, Bhagya Lakshmi

Institution: Aditya University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

OpenAlex ID: W7080009214

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