Special Session on Emotion and Sentiment in Intelligent Systems and Big Social Data Analysis (SentISData 2016)
Résumé
Aims and Scope : The rise of social media and the availability of big social data represent a challenge and a push forward for research on emotion and sentiment, which can meaningfully contribute to the investigation on affective cognitive models and their integration into intelligent systems. Social media are typical contexts for the emergence of subjective and expressive dimensions, but both the huge amount of data available and the relative dilution, within these data, of the meaning to be extracted, have to be carefully taken into account. Moreover, the role of the affective dimension is crucial also for systems interacting with humans in communicating data, where affect can contribute to convey the complex meanings underlying data. This calls for delving into the evolution of approaches, techniques and tools for modeling and analyzing emotion and sentiment, with the aim of dealing with the affective information conveyed by media that reflect spontaneous, unstructured user responses, and applying big social data analysis within a dynamic corpus of contents, created and enriched by users according to new paradigms of interactions fostering emotional engagement. Big social data analysis is interdisciplinary and combines areas such as natural language processing, social network analysis, multimedia management, social media analytics, trend discovery, information retrieval, computational linguistics. The goal of this special session is to collect contributions on the development and application of techniques for analyzing big social data, with a special focus on sentiment analysis and opinion mining, and on research about paradigms for the integration of emotional states in intelligent systems, to improve systems both for what concerns emotion-awareness and affective human-computer interaction.