emotionVis

Designing an Emotion Text Inference Tool for Visual Analytics

Chris Zimmerman, Mari-Klara Stein, Daniel Hardt, Christian de Fries Danielsen, Ravi Vatrapu

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

    Resumé

    With increasingly high volumes of conversations across social media, the rapid detection of emotions is of significant strategic value to industry practitioners. 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.
    OriginalsprogEngelsk
    TitelTackling Society’s Grand Challenges with Design Science : Proceedings of the 11th International Conference, DESRIST 2016
    RedaktørerJeffrey Parsons, Tuure Tuunanen, John Venable, Brian Donnellan, Markus Helfert, Jim Kenneally
    Udgivelses stedChams
    ForlagSpringer Science+Business Media
    Publikationsdato2016
    Sider238–244
    ISBN (Trykt)9783319392936
    ISBN (Elektronisk)9783319392943
    DOI
    StatusUdgivet - 2016
    BegivenhedThe 11th International Conference on Design Science Research in Information Systems and Technology. DESRIST 2016 - St. John’s, NL, Canada
    Varighed: 23 maj 201625 jun. 2016
    Konferencens nummer: 11
    https://desrist2016.wordpress.com/

    Konference

    KonferenceThe 11th International Conference on Design Science Research in Information Systems and Technology. DESRIST 2016
    Nummer11
    LandCanada
    BySt. John’s, NL
    Periode23/05/201625/06/2016
    Internetadresse
    NavnLecture Notes in Computer Science
    Vol/bind9661
    ISSN0302-9743

    Citer dette

    Zimmerman, C., Stein, M-K., Hardt, D., Danielsen, C. D. F., & Vatrapu, R. (2016). emotionVis: Designing an Emotion Text Inference Tool for Visual Analytics. I J. Parsons, T. Tuunanen, J. Venable, B. Donnellan, M. Helfert, & J. Kenneally (red.), Tackling Society’s Grand Challenges with Design Science: Proceedings of the 11th International Conference, DESRIST 2016 (s. 238–244). Chams: Springer Science+Business Media. Lecture Notes in Computer Science, Bind. 9661 https://doi.org/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. red. / Jeffrey Parsons ; Tuure Tuunanen ; John Venable ; Brian Donnellan ; Markus Helfert ; Jim Kenneally. Chams : Springer Science+Business Media, 2016. s. 238–244 (Lecture Notes in Computer Science, Bind 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. i J Parsons, T Tuunanen, J Venable, B Donnellan, M Helfert & J Kenneally (red), 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, bind 9661, s. 238–244, The 11th International Conference on Design Science Research in Information Systems and Technology. DESRIST 2016, St. John’s, NL, Canada, 23/05/2016. https://doi.org/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. red. / Jeffrey Parsons; Tuure Tuunanen; John Venable; Brian Donnellan; Markus Helfert; Jim Kenneally. Chams : Springer Science+Business Media, 2016. s. 238–244 (Lecture Notes in Computer Science, Bind 9661).

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

    TY - GEN

    T1 - emotionVis

    T2 - Designing an Emotion Text Inference Tool for Visual Analytics

    AU - Zimmerman, Chris

    AU - Stein, Mari-Klara

    AU - Hardt, Daniel

    AU - Danielsen, Christian de Fries

    AU - Vatrapu, Ravi

    PY - 2016

    Y1 - 2016

    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.

    AB - 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.

    U2 - 10.1007/978-3-319-39294-3

    DO - 10.1007/978-3-319-39294-3

    M3 - Article in proceedings

    SN - 9783319392936

    SP - 238

    EP - 244

    BT - Tackling Society’s Grand Challenges with Design Science

    A2 - Parsons, Jeffrey

    A2 - Tuunanen, Tuure

    A2 - Venable, John

    A2 - Donnellan, Brian

    A2 - Helfert, Markus

    A2 - Kenneally, Jim

    PB - Springer Science+Business Media

    CY - Chams

    ER -

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