emotionVis: Designing an Emotion Text Inference Tool for Visual Analytics

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    With increasingly high volumes of conversations across social media, the rapid detection of emotions is of significant strategic value to industry prac‐ titioners. Summarizing large volumes of text with computational linguistics and visual analytics allows for several new possibilities from general trend detection to specific applications in marketing practice, such as monitoring product launches, campaigns and public relations milestones. After collecting 1.6 million user-tagged feelings from 12 million online posts that mention emotions, we utilized machine learning techniques towards building an automatic ‘feelings meter’; a tool for both researchers and practitioners to automatically detect emotional dimensions from text. Following several iterations, the test version has now taken shape as emotionVis, a dashboard prototype for inferring emotions from text while presenting the results for visual analysis.
    With increasingly high volumes of conversations across social media, the rapid detection of emotions is of significant strategic value to industry prac‐ titioners. Summarizing large volumes of text with computational linguistics and visual analytics allows for several new possibilities from general trend detection to specific applications in marketing practice, such as monitoring product launches, campaigns and public relations milestones. After collecting 1.6 million user-tagged feelings from 12 million online posts that mention emotions, we utilized machine learning techniques towards building an automatic ‘feelings meter’; a tool for both researchers and practitioners to automatically detect emotional dimensions from text. Following several iterations, the test version has now taken shape as emotionVis, a dashboard prototype for inferring emotions from text while presenting the results for visual analysis.
    LanguageEnglish
    Title of host publicationTackling Society’s Grand Challenges with Design Science : Proceedings of the 11th International Conference, DESRIST 2016
    EditorsJeffrey Parsons, Tuure Tuunanen, John Venable, Brian Donnellan, Markus Helfert, Jim Kenneally
    Place of PublicationChams
    PublisherSpringer Science+Business Media
    Date2016
    Pages238–244
    ISBN (Print)9783319392936
    ISBN (Electronic)9783319392943
    DOIs
    StatePublished - 2016
    EventThe 11th International Conference on Design Science Research in Information Systems and Technology. DESRIST 2016 - St. John’s, NL, Canada
    Duration: 23 May 201625 Jun 2016
    Conference number: 11
    https://desrist2016.wordpress.com/

    Conference

    ConferenceThe 11th International Conference on Design Science Research in Information Systems and Technology. DESRIST 2016
    Number11
    CountryCanada
    CitySt. John’s, NL
    Period23/05/201625/06/2016
    Internet address
    SeriesLecture Notes in Computer Science
    Volume9661
    ISSN0302-9743

    Cite this

    Zimmerman, C., Stein, M-K., Hardt, D., Danielsen, C. D. F., & Vatrapu, R. (2016). emotionVis: Designing an Emotion Text Inference Tool for Visual Analytics. In J. Parsons, T. Tuunanen, J. Venable, B. Donnellan, M. Helfert, & J. Kenneally (Eds.), Tackling Society’s Grand Challenges with Design Science: Proceedings of the 11th International Conference, DESRIST 2016 (pp. 238–244). Chams: Springer Science+Business Media. Lecture Notes in Computer Science, Vol.. 9661, DOI: 10.1007/978-3-319-39294-3
    Zimmerman, Chris ; Stein, Mari-Klara ; Hardt, Daniel ; Danielsen, Christian de Fries ; Vatrapu, Ravi. / emotionVis : Designing an Emotion Text Inference Tool for Visual Analytics. Tackling Society’s Grand Challenges with Design Science: Proceedings of the 11th International Conference, DESRIST 2016. editor / Jeffrey Parsons ; Tuure Tuunanen ; John Venable ; Brian Donnellan ; Markus Helfert ; Jim Kenneally. Chams : Springer Science+Business Media, 2016. pp. 238–244 (Lecture Notes in Computer Science, ???volume??? 9661).
    @inproceedings{e4d5fff2afd6491b9496caee91ead038,
    title = "emotionVis: Designing an Emotion Text Inference Tool for Visual Analytics",
    abstract = "With increasingly high volumes of conversations across social media, the rapid detection of emotions is of significant strategic value to industry prac‐ titioners. Summarizing large volumes of text with computational linguistics and visual analytics allows for several new possibilities from general trend detection to specific applications in marketing practice, such as monitoring product launches, campaigns and public relations milestones. After collecting 1.6 million user-tagged feelings from 12 million online posts that mention emotions, we utilized machine learning techniques towards building an automatic ‘feelings meter’; a tool for both researchers and practitioners to automatically detect emotional dimensions from text. Following several iterations, the test version has now taken shape as emotionVis, a dashboard prototype for inferring emotions from text while presenting the results for visual analysis.",
    author = "Chris Zimmerman and Mari-Klara Stein and Daniel Hardt and Danielsen, {Christian de Fries} and Ravi Vatrapu",
    year = "2016",
    doi = "10.1007/978-3-319-39294-3",
    language = "English",
    isbn = "9783319392936",
    pages = "238–244",
    editor = "Jeffrey Parsons and Tuure Tuunanen and John Venable and Brian Donnellan and Markus Helfert and Jim Kenneally",
    booktitle = "Tackling Society’s Grand Challenges with Design Science",
    publisher = "Springer Science+Business Media",
    address = "Germany",

    }

    Zimmerman, C, Stein, M-K, Hardt, D, Danielsen, CDF & Vatrapu, R 2016, emotionVis: Designing an Emotion Text Inference Tool for Visual Analytics. in J Parsons, T Tuunanen, J Venable, B Donnellan, M Helfert & J Kenneally (eds), Tackling Society’s Grand Challenges with Design Science: Proceedings of the 11th International Conference, DESRIST 2016. Springer Science+Business Media, Chams, Lecture Notes in Computer Science, vol. 9661, pp. 238–244, St. John’s, NL, Canada, 23/05/2016. DOI: 10.1007/978-3-319-39294-3

    emotionVis : Designing an Emotion Text Inference Tool for Visual Analytics. / Zimmerman, Chris; Stein, Mari-Klara; Hardt, Daniel; Danielsen, Christian de Fries; Vatrapu, Ravi.

    Tackling Society’s Grand Challenges with Design Science: Proceedings of the 11th International Conference, DESRIST 2016. ed. / Jeffrey Parsons; Tuure Tuunanen; John Venable; Brian Donnellan; Markus Helfert; Jim Kenneally. Chams : Springer Science+Business Media, 2016. p. 238–244.

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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    N2 - With increasingly high volumes of conversations across social media, the rapid detection of emotions is of significant strategic value to industry prac‐ titioners. Summarizing large volumes of text with computational linguistics and visual analytics allows for several new possibilities from general trend detection to specific applications in marketing practice, such as monitoring product launches, campaigns and public relations milestones. After collecting 1.6 million user-tagged feelings from 12 million online posts that mention emotions, we utilized machine learning techniques towards building an automatic ‘feelings meter’; a tool for both researchers and practitioners to automatically detect emotional dimensions from text. Following several iterations, the test version has now taken shape as emotionVis, a dashboard prototype for inferring emotions from text while presenting the results for visual analysis.

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    Zimmerman C, Stein M-K, Hardt D, Danielsen CDF, Vatrapu R. emotionVis: Designing an Emotion Text Inference Tool for Visual Analytics. In Parsons J, Tuunanen T, Venable J, Donnellan B, Helfert M, Kenneally J, editors, Tackling Society’s Grand Challenges with Design Science: Proceedings of the 11th International Conference, DESRIST 2016. Chams: Springer Science+Business Media. 2016. p. 238–244. (Lecture Notes in Computer Science, Vol. 9661). Available from, DOI: 10.1007/978-3-319-39294-3