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Title: Sentiment analysis of student evaluation of teaching
Author: Ikauniece, Indra
Director/Tutor: Kovachev, Venelin
Puertas i Prats, Eloi
Martí Antonin, M. Antònia
Keywords: Lingüística computacional
Tractament del llenguatge natural (Informàtica)
Tesis de màster
Avaluació dels professors
Dades massives
Computational linguistics
Natural language processing (Computer science)
Masters theses
Teacher evaluation
Big data
Issue Date: 1-Jul-2018
Abstract: [en] After every semester in University of Barcelona all students are asked to fill a survey about professors and subjects from the previous semester. Students provide evaluation by answering two different kinds of questions - quantitative (numeric), and qualitative (open text). It would be useful for the professors, the program coordinators, and for the directors of the departments to have an automatic quantitative overview of the textual answers. The goals of this project are twofold: 1) to create a supervised dataset for sentiment analysis and polarity detection of student opinions in two languages (Catalan and Spanish); and 2) to validate the dataset empirically and propose competitive baselines by investigating, implementing and comparing sentiment analysis algorithms and methods to automatically classify student comments as positive, negative or neutral.
Note: Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2018, Tutor: Venelin Kovachev, Eloi Puertas i Prats i M. Antònia Martí Antonin
Appears in Collections:Màster Oficial - Fonaments de la Ciència de Dades
Programari - Treballs de l'alumnat

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