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Applied Computational Intelligence and Soft Computing
Volume 2016, Article ID 2385429, 12 pages
http://dx.doi.org/10.1155/2016/2385429
Research Article

Lexicon-Based Sentiment Analysis of Teachers’ Evaluation

Faculty of Computer Science, Institute of Business Administration (IBA), Garden/Kiyani Shaheed Road, Karachi 74400, Pakistan

Received 13 July 2016; Revised 31 August 2016; Accepted 5 September 2016

Academic Editor: Francesco Carlo Morabito

Copyright © 2016 Quratulain Rajput et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The end of the course evaluation has become an integral part of education management in almost every academic institution. The existing automated evaluation method primarily employs the Likert scale based quantitative scores provided by students about the delivery of the course and the knowledge of the instructor. The feedback is subsequently used to improve the quality of the teaching and often for the annual appraisal process. In addition to the Likert scale questions, the evaluation form typically contains open-ended questions where students can write general comments/feedback that might not be covered by the fixed questions. The textual feedback, however, is usually provided to teachers and administration and due to its nonquantitative nature is frequently not 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 aggregated Likert scale scores, but also provides new insight into a teacher’s performance with the help of tag clouds, sentiment score, and other frequency-based filters.