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

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

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)
DOI: 10.4018/978-1-7998-4730-4.ch020
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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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Sentiment analysis, or opinion mining, is the process of automatically extracting opinions using artificial intelligence and natural language processing to understand expressed emotions better, primarily online. Microsoft (2019) states that natural language processing is a tool that aims to design and build software that can analyze, understand, and generate languages ​​that humans can naturally use so that they can eventually communicate with a computer as if it did with another.

An opinion is a “view, judgment, or appraisal formed in mind about a particular matter” (Merriam-Webster, 2019). Liu (2004) classifies opinions into two types:

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