Lexicon-Based Sentiment Analysis of Teachers’ Evaluation

The end of the course evaluation has become an integral part of education management in almost every academic institution. Theexisting automated evaluation method primarily employs the Likert scale based quantitative scores provided by students aboutthe delivery of the course and the knowledge of the instructor. The feedback is subsequently used to improve the quality of theteaching and often for the annual appraisal process. In addition to the Likert scale questions, the evaluation form typically containsopen-ended questions where students can write general comments/feedback that might not be covered by the fixed questions. Thetextual feedback, however, is usually provided to teachers and administration and due to its nonquantitative nature is frequentlynot processed to gain more insight. This paper aims to address this aspect by applying several text analytics methods on students’feedback. The paper not only presents a sentiment analysis based metric, which is shown to be highly correlated with the aggregatedLikert scale scores, but also provides new insight into a teacher’s performance with the help of tag clouds, sentiment score, and otherfrequency-based filters.