  Emotions play a significant role in teamwork and collaborative activities
like software development. While researchers have analyzed developer emotions
in various software artifacts (e.g., issues, pull requests), few studies have
focused on understanding the broad spectrum of emotions expressed in chats. As
one of the most widely used means of communication, chats contain valuable
information in the form of informal conversations, such as negative
perspectives about adopting a tool. In this paper, we present a dataset of
developer chat messages manually annotated with a wide range of emotion labels
(and sub-labels), and analyze the type of information present in those
messages. We also investigate the unique signals of emotions specific to chats
and distinguish them from other forms of software communication. Our findings
suggest that chats have fewer expressions of Approval and Fear but more
expressions of Curiosity compared to GitHub comments. We also notice that
Confusion is frequently observed when discussing programming-related
information such as unexpected software behavior. Overall, our study highlights
the potential of mining emotions in developer chats for supporting software
maintenance and evolution tools.
