Opinion Mining for Instructor Evaluations at the Autonomous University of Ciudad Juarez

Abstract

The Autonomous University of Ciudad Juárez performs an instructor evaluation each semester to find strengths, weaknesses, and areas of opportunity during the teaching process. In this chapter, the authors show how opinion mining can be useful for labeling student comments as positives and negatives. For this purpose, a database was created using real opinions obtained from five professors of the UACJ over the last four years, covering a total of 20 subjects. Natural language processing techniques were used on the database to normalize its data. Experimental results using 1-NN and Bagging classifiers shows that it is possible to automatically label positive and negative comments with an accuracy of 80.13%.