Fecha: 13 de Junio 2016

Ponente: Dra. Miriam Fernández (Research Fellow at Knowledge Medio Institute (KMi), The Open University, UK )

Lugar de celebraciónSalón de Actos y de Grados de la Facultad de Educación y Sala J. Mira de la ETSI de Informática de la UNED


Social media platforms are now considered among the most popular forms of online communication. This has recently brought great opportunities to companies interested in tracking and monitoring the reputation of their brands and businesses, and to policy makers and politicians to support their assessment of public opinions about their policies or political issues. A wide range of approaches to sentiment analysis over social streams, and particular Twitter, have been recently built. Most of these approaches rely mainly on the presence of affect words or syntactic structures that explicitly and unambiguously reflect sentiment (e.g., “great”, “terrible”). However, these approaches are semantically weak, that is, they do not account for the semantics of words when detecting their sentiment in text. This is problematic since the sentiment of words, in many cases, is associated with their semantics, either along the context they occur within (e.g., “great” is negative in the context “pain”) or the conceptual meaning associated with the words (e.g., “Ebola“ is negative when its associated semantic concept is “Virus“). This talk discusses the role of words’ semantics in sentiment analysis of microblogs, aiming mainly at addressing the above problem. In particular, Twitter is used as a case study of microblogging platforms to investigate whether capturing the sentiment of words with respect to their semantics leads to more accurate sentiment analysis models on Twitter. To this end, several approaches are proposed in this talk for extracting and incorporating two types of word semantics for sentiment analysis: contextual semantics (i.e., semantics captured from words’ co-occurrences) and conceptual semantics (i.e., semantics extracted from external knowledge sources).