Challenges in providing automatic affective feedback in instant messaging applications

Chieh Yang Huang, Kenneth Huang, Lun Wei Ku

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 Emotion Push, 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.

Original languageEnglish (US)
Title of host publicationSS-17-01
Subtitle of host publicationArtificial Intelligene for the Social Good; SS-17-02: Computational Construction Grammar and Natural Language Understanding; SS-17-03: Computational Context: Why It's Important, What It Means, and Can It Be Computed?; SS-17-04: Designing the User Experience of Machine Learning Systems; SS-17-05: Interactive Multisensory Object Perception for Embodied Agents; SS-17-06: Learning from Observation of Humans; SS-17-07: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence; SS-17-08: Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing
PublisherAI Access Foundation
Pages382-388
Number of pages7
ISBN (Electronic)9781577357797
StatePublished - Jan 1 2017
Event2017 AAAI Spring Symposium - Stanford, United States
Duration: Mar 27 2017Mar 29 2017

Publication series

NameAAAI Spring Symposium - Technical Report
VolumeSS-17-01 - SS-17-08

Conference

Conference2017 AAAI Spring Symposium
CountryUnited States
CityStanford
Period3/27/173/29/17

Fingerprint

Feedback
Smartphones
Communication

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Huang, C. Y., Huang, K., & Ku, L. W. (2017). Challenges in providing automatic affective feedback in instant messaging applications. In SS-17-01: Artificial Intelligene for the Social Good; SS-17-02: Computational Construction Grammar and Natural Language Understanding; SS-17-03: Computational Context: Why It's Important, What It Means, and Can It Be Computed?; SS-17-04: Designing the User Experience of Machine Learning Systems; SS-17-05: Interactive Multisensory Object Perception for Embodied Agents; SS-17-06: Learning from Observation of Humans; SS-17-07: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence; SS-17-08: Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing (pp. 382-388). (AAAI Spring Symposium - Technical Report; Vol. SS-17-01 - SS-17-08). AI Access Foundation.
Huang, Chieh Yang ; Huang, Kenneth ; Ku, Lun Wei. / Challenges in providing automatic affective feedback in instant messaging applications. SS-17-01: Artificial Intelligene for the Social Good; SS-17-02: Computational Construction Grammar and Natural Language Understanding; SS-17-03: Computational Context: Why It's Important, What It Means, and Can It Be Computed?; SS-17-04: Designing the User Experience of Machine Learning Systems; SS-17-05: Interactive Multisensory Object Perception for Embodied Agents; SS-17-06: Learning from Observation of Humans; SS-17-07: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence; SS-17-08: Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing. AI Access Foundation, 2017. pp. 382-388 (AAAI Spring Symposium - Technical Report).
@inproceedings{45bb17df4ca54d8499b9603a9fd4a84a,
title = "Challenges in providing automatic affective feedback in instant messaging applications",
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 Emotion Push, 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.",
author = "Huang, {Chieh Yang} and Kenneth Huang and Ku, {Lun Wei}",
year = "2017",
month = "1",
day = "1",
language = "English (US)",
series = "AAAI Spring Symposium - Technical Report",
publisher = "AI Access Foundation",
pages = "382--388",
booktitle = "SS-17-01",
address = "United States",

}

Huang, CY, Huang, K & Ku, LW 2017, Challenges in providing automatic affective feedback in instant messaging applications. in SS-17-01: Artificial Intelligene for the Social Good; SS-17-02: Computational Construction Grammar and Natural Language Understanding; SS-17-03: Computational Context: Why It's Important, What It Means, and Can It Be Computed?; SS-17-04: Designing the User Experience of Machine Learning Systems; SS-17-05: Interactive Multisensory Object Perception for Embodied Agents; SS-17-06: Learning from Observation of Humans; SS-17-07: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence; SS-17-08: Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing. AAAI Spring Symposium - Technical Report, vol. SS-17-01 - SS-17-08, AI Access Foundation, pp. 382-388, 2017 AAAI Spring Symposium, Stanford, United States, 3/27/17.

Challenges in providing automatic affective feedback in instant messaging applications. / Huang, Chieh Yang; Huang, Kenneth; Ku, Lun Wei.

SS-17-01: Artificial Intelligene for the Social Good; SS-17-02: Computational Construction Grammar and Natural Language Understanding; SS-17-03: Computational Context: Why It's Important, What It Means, and Can It Be Computed?; SS-17-04: Designing the User Experience of Machine Learning Systems; SS-17-05: Interactive Multisensory Object Perception for Embodied Agents; SS-17-06: Learning from Observation of Humans; SS-17-07: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence; SS-17-08: Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing. AI Access Foundation, 2017. p. 382-388 (AAAI Spring Symposium - Technical Report; Vol. SS-17-01 - SS-17-08).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Challenges in providing automatic affective feedback in instant messaging applications

AU - Huang, Chieh Yang

AU - Huang, Kenneth

AU - Ku, Lun Wei

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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 Emotion Push, 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.

AB - 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 Emotion Push, 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.

UR - http://www.scopus.com/inward/record.url?scp=85028703311&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85028703311&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85028703311

T3 - AAAI Spring Symposium - Technical Report

SP - 382

EP - 388

BT - SS-17-01

PB - AI Access Foundation

ER -

Huang CY, Huang K, Ku LW. Challenges in providing automatic affective feedback in instant messaging applications. In SS-17-01: Artificial Intelligene for the Social Good; SS-17-02: Computational Construction Grammar and Natural Language Understanding; SS-17-03: Computational Context: Why It's Important, What It Means, and Can It Be Computed?; SS-17-04: Designing the User Experience of Machine Learning Systems; SS-17-05: Interactive Multisensory Object Perception for Embodied Agents; SS-17-06: Learning from Observation of Humans; SS-17-07: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence; SS-17-08: Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing. AI Access Foundation. 2017. p. 382-388. (AAAI Spring Symposium - Technical Report).