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

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Author(s): Rafael Jiménez (Universidad Autónoma de Ciudad Juárez, Mexico), Vicente García (Universidad Autónoma de Ciudad Juárez, Mexico), Abraham López (Universidad Autónoma de Ciudad Juárez, Mexico), Alejandra Mendoza Carreón (Universidad Autónoma de Ciudad Juárez, Mexico)and Alan Ponce (Universidad Autónoma de Ciudad Juárez, Mexico)
Copyright: 2022
Pages: 17
Source title: Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-6303-1.ch066

Keywords: Data Analysis and Statistics / Data Mining / Engineering Science Reference / Library & Information Science

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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%.

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

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