  The continuous and increasing use of social media has enabled the expression
of human thoughts, opinions, and everyday actions publicly at an unprecedented
scale. We present the Vent dataset, the largest annotated dataset of text,
emotions, and social connections to date. It comprises more than 33 millions of
posts by nearly a million of users together with their social connections. Each
post has an associated emotion. There are 705 different emotions, organized in
63 "emotion categories", forming a two-level taxonomy of affects. Our initial
statistical analysis describes the global patterns of activity in the Vent
platform, revealing large heterogenities and certain remarkable regularities
regarding the use of the different emotions. We focus on the aggregated use of
emotions, the temporal activity, and the social network of users, and outline
possible methods to infer emotion networks based on the user activity. We also
analyze the text and describe the affective landscape of Vent, finding
agreements with existing (small scale) annotated corpus in terms of emotion
categories and positive/negative valences. Finally, we discuss possible
research questions that can be addressed from this unique dataset.
