Linked Data Models for Sentiment and Emotion Analysis in Social Networks

Carlos A. Iglesias, J. Fernando Sánchez-Rada, Gabriela Vulcu & Paul Buitelaar (2016). Linked Data Models for Sentiment and Emotion Analysis in Social Networks. In Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina & Bing Liu (editors), Sentiment Analysis in Social Networks. Morgan Kauffman.

Language resource interoperability is still a major challenge in sentiment analysis. One of the current trends for solving this issue is the adoption of a linked data perspective for semantically modeling, interlinking, and publishing lexical and other linguistic resources. This chapter contributes to the development of the linguistic linked open data through a linked data model for sentiment and emotion analysis in social networks that is based on two vocabularies: Marl and Onyx for sentiment and emotion modeling respectively. These vocabularies are used for (1) affective corpus annotation, (2) affective lexicon annotation, and (3) sentiment and emotion services interoperability. Several aspects of the solution are discussed, such as the transformation of legacy resources, the generation of domain-specific sentiment lexicons, and the benefits of interlinking language resources for sentiment analysis with other resources such as WordNet or DBpedia.