Hybrid Lexicon and Transformer-Based Sentiment Analysis of Student Feedback for Faculty Evaluation: A Speech-to-Text Approach

Abstract

The conventional methods for assessing the performance of educators in academic settings have often been hindered by outdated feedback systems, leading to assessments that can be either skewed or in- complete. This study unveils an innovative, dual-approach methodology that fuses lexicon-driven sentiment analysis with cutting-edge Transformer architectures, specifically focusing on BERT (Bidirectional Encoder Representations from Transformers). This approach provides a more sophisticated, real-time assessment of educators based on student evaluations. One of the standout features of this study is the integration of Speech- to-Text technologies, which allows for the immediate transformation of verbal feedback into text that can be analyzed. The approach makes use of a specialized Educational Sentiment Lexicon for preliminary sentiment evaluation, which subsequently refines the performance of a pre-existing BERT model. This dual-model is proficient in scrutinizing both text-based and verbal feedback—the latter being converted into text through advanced Speech-to-Text techniques. Our results indicate that this approach significantly outperforms existing lexicon-based and machine learning methods in terms of accuracy and comprehensiveness. By providing a real-time, versatile sentiment analysis tool tailored for educational settings, this research marks a paradigm shift in the scope and quality of faculty evaluations, thereby contributing substantially to the field of educational technology and sentiment analysis.