Faculty Feedback Mining System using Sentiment Analysis

Abstract

In higher education, the continuous improvement of teaching quality is paramount for fostering a conducive learning environment. This study presents a Faculty Feedback Extraction System (FFES) that utilizes state-of-the-art sentiment analysis techniques to derive actionable insights from faculty feedback. The system uses natural language processing (NLP) and machine learning algorithms to analyze and classify emotions, giving schools a better understanding of teachers' emotions and fostering a culture of continuous improvement. The FFMS employs a multifaceted sentiment analysis approach, considering both textual and contextual features to ensure nuanced interpretation of faculty feedback. Leveraging a diverse dataset of faculty evaluations, the system uses the highest sentiment rating standards to classify emotions into positive, negative or neutral categories. This research focuses on to apply the approach which allows schools to better understand teachers' views and helps influence professional and overall development. FFMS provides deep learning models such as neural network (RNN) and tracking techniques to improve the accuracy and depth of emotion classification. These models enable the system to capture subtle nuances in feedback, providing a more nuanced understanding of faculty sentiments. Additionally, the FFMS facilitates real-time monitoring and analysis through intuitive dashboards, empowering academic administrators to make informed decisions based on the latest feedback trends.