Discovery and Classification of the Underlying Emotions in the User Generated Content (UGC)

Abstract

Nowadays the User Generated Content (UGC) is growing very fast in Internet. Social networks have become a valuable source for knowledge but there is a big gap in the automatic emotional analysis of online textual content. The aim of this research is to determine the emotional qualities of tourists in the perceptions and experiences that underlie in the UGC, through the automatic identification of emotions in twitter texts. The methodology is a quantitative and qualitative content analysis using affective computing techniques. This paper demonstrates empirically the feasibility of the automatic identification of the underlying emotions in the discourses generated by the (UGC), through a powerful ad-hoc software combining Natural language Processing and affective computing field tools. Furthermore, our approach enriches the classification Parrot and Plutchik categorization framework.