Tesis profesional presentada por María José Díaz Torres [maria.diazto@udlap.mx]

Miembro del programa de honores. Licenciatura en Idiomas. Departamento de Lenguas. Escuela de Artes y Humanidades, Universidad de las Américas Puebla.

Jurado Calificador

Presidente: Dr. Antonio Rico Sulayes
Vocal y Director: Dra. Ofelia Delfina Cervantes Villagómez
Secretario: Dr. Esteban Castillo Juarez

Cholula, Puebla, México a 14 de mayo de 2019.

Resumen

This study describes a sentiment analysis service that is part of a learning analytics platform developed for the Uruguayan educational system, and proposes four new localized sentiment classification models. The sentiment analysis service performs the natural language processing task of determining the attitude or sentiment associated to a text, in this case, the sentiments of student-generated comments as a result of their interactions in several learning management systems and social media. The methodology of the original sentiment classifier is discussed...

Palabras clave: linguistic variation, machine learning, Rioplatense Spanish, sentiment analysis, social learning analytics, Spanish, Uruguay.

Resumen.

Índice de contenido

Portada

Agradecimientos

Índices

Capítulo 1. Introduction

Capítulo 2. Related Work

  • 2.1 Learning Management Systems
  • 2.2 Social Learning Analytics
  • 2.3 Artificial Intelligence, Machine Learning, and Natural Language Processing
  • 2.4 Sentiment Analysis
  • 2.5 Sentiment Analysis in Educational Research

Capítulo 3. The DIIA Proposal

  • 3.1 General Architecture
  • 3.2 Platform Visualization

Capítulo 4. The DIIA Sentiment Analysis Methodology

  • 4.1 Dataset Selection
  • 4.2 Dataset Preprocessing
  • 4.3 Feature Selection and Representation
  • 4.4 DIIA´s Sentiment Classifier Using a Supervised Learning Approach
  • 4.5 Evaluation and Results

Capítulo 5. Linguistic Framework for the Localization Proposal

  • 5.1 Linguistic Variation
  • 5.2 Spanish in Uruguay

Capítulo 6. Sentiment Classifier Localization Methodology

  • 6.1 Dataset Selection
  • 6.2 Dataset Preprocessing
  • 6.3 Feature Selection and Representation
  • 6.4 Localized Sentiment Classification Model
  • 6.5 Evaluation and Results

Capítulo 7. Discussion

Capítulo 8. Conclusions

Capítulo 9. Future Work

Referencias

Apéndice 1. TreeTagger´s Spanish Tagset (Schmid, n. d.)

Documento completo (archivo pdf, 541 kb)

Díaz Torres, M. J. 2019. Contributions to Social Learning Analytics based on Sentiment Analysis of Students´ Interactions in Educational Environments. Tesis Licenciatura. Idiomas. Departamento de Lenguas, Escuela de Artes y Humanidades, Universidad de las Américas Puebla. Mayo. Derechos Reservados © 2019.