Changeable Polarity of Verbs through Emotions' Attribution in Crowdsourcing Experiments

Sentiment analysis and emotion detection are tasks with common features but rarely related because they tend to categorize the objects of their studies according to different categories, i.e. positive, negative and neutral values in SA, and emotion labels such as "joy", "anger" etc. in emotion detection. In this paper we try to bridge this gap, reporting on three crowdsourcing experiments to collect speakers' intuitions on emotion(s) associated with events denoted by verbs and propose to set contextual polarity values on the basis of the selected emotions. In this way we suggest a methodology to handle connotational meanings of verbs that can help to refine automatic sentiment analysis on social media, where shared contents are often short reports on pleasant or unpleasant events and activities.