A Sentiment Analysis Model to Analyze Students Reviews of Teacher Performance Using Support Vector Machines
| Autor: | Guadalupe Gutiérrez Esparza, Marco Álvarez, Julio Ponce, Edgar Cossio, Alberto Hernández, Alberto Ochoa Zezzatti, Jose de Jesus Nava, Alejandro de-Luna |
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| Rok vydání: | 2017 |
| Předmět: |
Polynomial
Higher education business.industry Process (engineering) Computer science 05 social sciences Sentiment analysis 050301 education 02 engineering and technology Machine learning computer.software_genre Constructive Support vector machine Order (business) ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Sensibility Artificial intelligence business 0503 education computer |
| Zdroj: | Distributed Computing and Artificial Intelligence, 14th International Conference ISBN: 9783319624099 DCAI |
| Popis: | Teacher evaluation is considered an important process in higher education institutions to know about teacher performance and implement constructive strategies in order to benefit students in their education. The opinion of students has become one of the main factors to consider when evaluating teachers. In this study we present a Model called SocialMining using a corpus of real comments in Spanish about teacher performance assessment. We applied Support Vector Machines algorithm with three kernels: linear, radial and polynomial, to predict a classification of comments in positive, negative or neutral. We calculated sensibility, specificity and predictive values as evaluation measures. The results of this work may help other experiments to improve the classification process of comments and suggest teacher improvement courses for teachers. |
| Databáze: | OpenAIRE |
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