Moodswipe: A soft keyboard that suggests messages based on user-specified emotions

Chieh Yang Huang, Tristan Labetoulle, Ting Hao Huang, Yi Pei Chen, Hung Chen Chen, Vallari Srivastava, Lun Wei Ku

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

1 Citation (Scopus)

Abstract

We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data. The aim of MoodSwipe is to create a convenient user interface to enjoy the technology of emotion classification and text suggestion, and at the same time to collect labeled data automatically for developing more advanced technologies. While users select the MoodSwipe keyboard, they can type as usual but sense the emotion conveyed by their text and receive suggestions for their message as a benefit. In MoodSwipe, the detected emotions serve as the medium for suggested texts, where viewing the latter is the incentive to correcting the former. We conduct several experiments to show the superiority of the emotion classification models trained on the dialog data, and further to verify good emotion cues are important context for text suggestion.

Original languageEnglish (US)
Title of host publicationEMNLP 2017 - Conference on Empirical Methods in Natural Language Processing
Subtitle of host publicationSystem Demonstrations, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages73-78
Number of pages6
ISBN (Electronic)9781945626975
StatePublished - Jan 1 2017
Event2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2017 - Copenhagen, Denmark
Duration: Sep 9 2017Sep 11 2017

Publication series

NameEMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings

Conference

Conference2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2017
CountryDenmark
CityCopenhagen
Period9/9/179/11/17

Fingerprint

User interfaces
Experiments

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

Cite this

Huang, C. Y., Labetoulle, T., Huang, T. H., Chen, Y. P., Chen, H. C., Srivastava, V., & Ku, L. W. (2017). Moodswipe: A soft keyboard that suggests messages based on user-specified emotions. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings (pp. 73-78). (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings). Association for Computational Linguistics (ACL).
Huang, Chieh Yang ; Labetoulle, Tristan ; Huang, Ting Hao ; Chen, Yi Pei ; Chen, Hung Chen ; Srivastava, Vallari ; Ku, Lun Wei. / Moodswipe : A soft keyboard that suggests messages based on user-specified emotions. EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings. Association for Computational Linguistics (ACL), 2017. pp. 73-78 (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings).
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title = "Moodswipe: A soft keyboard that suggests messages based on user-specified emotions",
abstract = "We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data. The aim of MoodSwipe is to create a convenient user interface to enjoy the technology of emotion classification and text suggestion, and at the same time to collect labeled data automatically for developing more advanced technologies. While users select the MoodSwipe keyboard, they can type as usual but sense the emotion conveyed by their text and receive suggestions for their message as a benefit. In MoodSwipe, the detected emotions serve as the medium for suggested texts, where viewing the latter is the incentive to correcting the former. We conduct several experiments to show the superiority of the emotion classification models trained on the dialog data, and further to verify good emotion cues are important context for text suggestion.",
author = "Huang, {Chieh Yang} and Tristan Labetoulle and Huang, {Ting Hao} and Chen, {Yi Pei} and Chen, {Hung Chen} and Vallari Srivastava and Ku, {Lun Wei}",
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series = "EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings",
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Huang, CY, Labetoulle, T, Huang, TH, Chen, YP, Chen, HC, Srivastava, V & Ku, LW 2017, Moodswipe: A soft keyboard that suggests messages based on user-specified emotions. in EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings. EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings, Association for Computational Linguistics (ACL), pp. 73-78, 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2017, Copenhagen, Denmark, 9/9/17.

Moodswipe : A soft keyboard that suggests messages based on user-specified emotions. / Huang, Chieh Yang; Labetoulle, Tristan; Huang, Ting Hao; Chen, Yi Pei; Chen, Hung Chen; Srivastava, Vallari; Ku, Lun Wei.

EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings. Association for Computational Linguistics (ACL), 2017. p. 73-78 (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings).

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

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AB - We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data. The aim of MoodSwipe is to create a convenient user interface to enjoy the technology of emotion classification and text suggestion, and at the same time to collect labeled data automatically for developing more advanced technologies. While users select the MoodSwipe keyboard, they can type as usual but sense the emotion conveyed by their text and receive suggestions for their message as a benefit. In MoodSwipe, the detected emotions serve as the medium for suggested texts, where viewing the latter is the incentive to correcting the former. We conduct several experiments to show the superiority of the emotion classification models trained on the dialog data, and further to verify good emotion cues are important context for text suggestion.

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Huang CY, Labetoulle T, Huang TH, Chen YP, Chen HC, Srivastava V et al. Moodswipe: A soft keyboard that suggests messages based on user-specified emotions. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings. Association for Computational Linguistics (ACL). 2017. p. 73-78. (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings).