Machine learning methods for understanding social media communication: Modeling irony and emojis

• Autores: Francesco Barbieri
• Directores de la Tesis: Horacio Saggion (dir. tes.)
• Lectura: En la Universitat Pompeu Fabra ( España ) en 2018
• Idioma: español
• Tribunal Calificador de la Tesis: Pablo Gervás Gómez-Navarro (presid.), Dirk Hovy (secret.), Ricardo Baeza Yates (voc.)
• Programa de doctorado: Programa de Doctorado en Tecnologías de la Información y las Comunicaciones por la Universidad Pompeu Fabra
• Materias:
  • Ciencias tecnológicas
    • Tecnología de los ordenadores
      • Ordenadores digitales
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • In this dissertation we propose algorithms for the analysis of social media texts, focusing on two particular aspects: irony and emojis. We propose novel automatic systems, based on machine learning methods, able to recognize and interpret these two phenomena. We also explore the problem of topic bias in sentiment analysis and irony detection, showing that traditional word based systems are not robust when they have to recognize irony on a new domain. We argue that our proposal is better suited for topic changes. We then use our approach to recognize another phenomena related to irony: satirical news in Twitter. By relying on distributional semantic models, we also introduce a novel method for the study of the meaning and use of emojis in social media texts. Moreover, we also propose an emoji prediction task that consists in predicting the emoji present in a text message using only the text. We have shown that this emoji prediction task can be performed by deep-learning systems with good accuracy, and that this accuracy can be improved by using images included in the post.