Challenges in Providing Automatic Affective Feedback in Instant Messaging Applications

TL;DR

This paper explores the challenges of implementing automatic emotion detection in instant messaging, highlighting issues like emotion complexity, multi-user dynamics, and misclassification, based on a deployment study of the EmotionPush system.

Contribution

It identifies five key challenges in deploying emotion sensing in instant messaging and provides insights for future research in affective computing and conversational emotion sensing.

Findings

Identified five major challenges in emotion detection for messaging apps.

Provided real-world insights from a two-week deployment study.

Highlighted issues like emotion continuum and misclassification.

Abstract

Instant messaging is one of the major channels of computer mediated communication. However, humans are known to be very limited in understanding others' emotions via text-based communication. Aiming on introducing emotion sensing technologies to instant messaging, we developed EmotionPush, a system that automatically detects the emotions of the messages end-users received on Facebook Messenger and provides colored cues on their smartphones accordingly. We conducted a deployment study with 20 participants during a time span of two weeks. In this paper, we revealed five challenges, along with examples, that we observed in our study based on both user's feedback and chat logs, including (i)the continuum of emotions, (ii)multi-user conversations, (iii)different dynamics between different users, (iv)misclassification of emotions and (v)unconventional content. We believe this discussion will benefit the future exploration of affective computing for instant messaging, and also shed light on research of conversational emotion sensing.