OHMAN, Emily

写真a

Affiliation

Faculty of International Research and Education, School of International Liberal Studies

Job title

Assistant Professor(tenure-track)
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Education 【 display / non-display 】

  • 2016.01
    -
    2021.03

    University of Helsinki   Department of Digital Humanities - Language Technology   PhD  

    "The Language of Emotions: Building and Applying Resources for Computational Approaches to Emotion Detection for English and Beyond"

  • 2016
    -
    2021

    University of Helsinki   HYPE (Centre for University Teaching and Learning)   30 credits of University pedagogy  

  • 2016.07
    　
    　

    Instituto Superior Técnico (IST)   Lisbon Machine Learning Summer School  

  • 　
    -
    2015.10

    Linnaeus University   Department of English   MA in English Linguistics  

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Research Experience 【 display / non-display 】

  • 2021.04
    -
    Now

    Waseda University   School of International Liberal Studies   Assistant Professor

  • 2020.12
    -
    2021.03

    Tampere University   Faculty of Information Technology and Communication Sciences   Intimacy in Data-driven Culture   Postdoc

  • 2020.09
    -
    2021.03

    University of Helsinki / Tampere University / Consumer Society Research Centre   Unconventional Communicators in the Corona Crisis (UnCoCo)   Postdoc

    Group grant

  • 2016.01
    -
    2021.03

    University of Helsinki   Department of Digital Humanities   PhD project in Language Technology   Doctoral Student

  • 2014.09
    -
    2015.12

    University of Helsinki   Department of English   Language Change Database   Research Assistant

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Professional Memberships 【 display / non-display 】

  • 2021
    -
    Now

    Japanese Association for Digital Humanities

  • 2020
    -
    Now

    European Association for Digital Humanities

  • 2018
    -
    Now

    Rajapinta ry (Computational Social Science)

  • 2018
    -
    Now

    Digital Humanities in the Nordic Countries

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Research Areas 【 display / non-display 】

  • Science education

  • Library and information science, humanistic and social informatics

  • Tertiary education

  • Linguistics

  • Intelligent robotics

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Research Interests 【 display / non-display 】

  • digital humanities, computational linguistics, language technology, computational social science, computational literary studies, NLP, sentiment analysis, emotion detection, machine learning

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Papers 【 display / non-display 】

  • XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection.

    Emily Öhman, Marc Pàmies, Kaisla Kajava, Jörg Tiedemann

        6542 - 6552  2020  [Refereed]

    Authorship：Lead author, Corresponding author

    DOI

  • Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation.

    Emily Öhman, Kaisla Kajava, Jörg Tiedemann, Timo Honkela

        24 - 30  2018  [Refereed]

    Authorship：Lead author, Corresponding author

    DOI

  • SELF & FEIL

    Öhman Emily

       2021.04

    Authorship：Lead author, Corresponding author

    　View Summary

    This paper introduces a Sentiment and Emotion Lexicon for Finnish (SELF) and a Finnish Emotion Intensity Lexicon (FEIL). We describe the lexicon creation process and evaluate the lexicon using some commonly available tools. The lexicon uses annotations projected from the NRC Emotion Lexicon with carefully edited translations. To our knowledge, this is the first comprehensive sentiment and emotion lexicon for Finnish.

  • The Language of Emotions

    Öhman Emily

       2021.03  [Refereed]  [International journal]

    Authorship：Lead author, Corresponding author

    　View Summary

    Emotions have always been central to the human experience: the ancient Greeks had philosophical debates about the nature of emotions and Charles Darwin can be said to have founded the modern theories of emotions with his study The expression of the emotions in man and animals. Theories of emotion are still actively researched in many different fields from psychology, cognitive science, and anthropology to computer science.

    Sentiment analysis usually refers to the use of computational tools to identify and extract sentiments and emotions from various modalities. In this dissertation, I use sentiment analysis in conjunction with natural language processing to identify, quantify, and classify emotions in text. Specifically, emotions are examined in multilingual settings using multidimensional models of emotions.

    Plutchik’s wheel of emotions and emotional intensities are used to classify emotions in parallel corpora via both lexical methods and supervised machine learning methods. By analyzing emotional language content in text, the connection between language and emotions can be better understood. I have developed new approaches to create a more equitable natural language processing approach for sentiment analysis, meaning the development and evaluation of massively multilingual annotated datasets, contributing to the provision of tools for under-resourced languages.

    This dissertation is comprised of ten articles on related topics in sentiment analysis. In these articles, I discuss lexicon-based methods and the creation of emotion and sentiment lexicons, the creation of datasets for supervised machine learning, the training of models for supervised machine learning, and the evaluation of such models. I also examine the annotation process in relation to creating datasets in-depth, including the creation of
    a lightweight easily deployed annotation platform. As an additional step, I test the different approaches in downstream applications.

    These practical applications include the study of political party rhetoric from the perspective of emotion words used and the intensities of those emotion words. I also examine how simple lexicon-based methods can be used to make the study of affect in literature less subjective. Additionally, I attempt to link sentiment analysis with hate speech detection and offensive speech target identification.

    The main contribution of this dissertation is in providing tools for sentiment analysis and in demonstrating how these tools can be augmented for use in a wide variety of languages and practical applications at low cost.

  • Emotion annotation: Rethinking emotion categorization

    Emily Öhman

    CEUR Workshop Proceedings   2865   134 - 144  2020  [Refereed]

    Authorship：Lead author, Corresponding author

    　View Summary

    One of the biggest hurdles for the utilization of machine learning in interdisciplinary projects is the need for annotated training data which is costly to create. Emotion annotation is a notoriously difficult task, and the current annotation schemes which are based on psychological theories of human interaction are not always the most conducive for the creation of reliable emotion annotations, nor are they optimal for annotating emotions in the modality of text. This paper discusses the theory, history, and challenges of emotion annotation, and proposes improvements for emotion annotation tasks based on both theory and case studies. These improvements focus on rethinking the categorization of emotions and the overlap and disjointedness of emotion categories.

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Works 【 display / non-display 】

  • FEIL (Finnish Emotion Intensity Lexicon)

    Emily Ohman  Other 

    2020
    -
    2021

  • SELF (Sentiment and Emotion Lexicon for Finnish)

    Emily Öhman  Other 

    2020
    -
    2021

  • XED multilingual emotion-annotated dataset

    Emily Ohman, Kaisla Kajava, Marc Pàmies, Jörg Tiedemann  Database science 

    2020
    -
    2021

  • Sentimentator (dockerized sentiment annotation tool)

    Emily Ohman, Kaisla Kajava  Software 

    2018
    　
    　

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Awards 【 display / non-display 】

  • Small grants

    2020.11   European Association for Digital Humanities   Python for digital humanities

  • Future Digileader

    2020.11   KTH Royal Institute of Technology, Digital Futures research center, Sweden   Future Digileader

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Research Projects 【 display / non-display 】

  • Unconventional Communicators in the COVID crisis

    Project Year :

    2020
    -
    2022
     

    Salla-Maaria Laaksonen, Juho Paakkonen, Emily Ohman, Essi Poyry,Hanna Reinikainen

    Authorship： Coinvestigator(s)

  • Intimacy in Data-driven Culture

    Project Year :

    2020
    -
    2021.03
     

    Anu Koivunen, Kaarina Nikunen

    Authorship： Collaborating Investigator(s) (not designated on Grant-in-Aid)

  • Salaried PhD position

    Project Year :

    2016.01
    -
    2020.11
     

    Authorship： Principal investigator

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Presentations 【 display / non-display 】

  • What to expect from an academic career?

    Maryam Elahi, Naveen Bagalkot, Emily Ohman  [Invited]

    Future Digileaders 

    Presentation date： 2021.10

  • Skin Deep: Exploring ideals of Japanese beauty through social media

    Amy Grace Metcalfe, Emily Ohman

    Japanese Association for Digital Humanities Conference 2021 

    Presentation date： 2021.09

  • Panel: Current approaches to Digital Humanities. Researches of the EADH Small Grants 2020 recipients

    Rada Varga, Anna-Maria Sichani, Merisa Martinez, Emily Ohman, Annamária –, Izabella Pázsint, Gamze Saygi, Oksana Maistat, Ilia Uchitel, Kathryn Simpson  [Invited]

    European Association for Digital Humanities 

    Presentation date： 2021.09

  • AI Ethics and Applications in Finance

    Emily Ohman  [Invited]

    INDEX Varainhoito 

    Presentation date： 2021.09

  • AI for the Environment

    Emily Ohman  [Invited]

    Finnish Environment Institute 

    Presentation date： 2021.02

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Syllabus 【 display / non-display 】

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Teaching Experience 【 display / non-display 】

  • Social Media and Data Analysis

    Waseda University  

    2021.09
    -
    Now
     

  • Python Programming for Digital Humanities

    Waseda University  

    2021.04
    -
    Now
     

  • Introduction to Digital Humanities

    Waseda University  

    2021.04
    -
    Now
     

  • Methods for Digital Humanities

    Waseda University  

    2021.04
    -
    Now
     

  • Citizen Science: Crowd-sourcing as a Tool for Collecting Quantitative and Qualitative Data

    University of Helsinki  

    2021.01
    -
    2021.04
     

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Committee Memberships 【 display / non-display 】

  • 2021.04
    -
    Now

    Waseda University  Statistics Education Committee

  • 　
    　
    　

    University of Helsinki  Ethics committee

  • 　
    　
    　

    University of Helsinki  Teaching Evaluation Committee

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Media Coverage 【 display / non-display 】

  • Lectio praecursoria: The Language of Emotions

    Internet

    Rajapinta ry. (Computational social science academic society)  

    https://rajapinta.co/2021/04/30/lectio-praecursoria-the-language-of-emotions/  

    2021.04

  • New methods improve analysis of emotional content in text

    Internet

    Author： Other  

    Finnish National News Agency (STT)  

    https://www.sttinfo.fi/tiedote/uusilla-metodeilla-voi-analysoida-tekstin-tunnelatauksia-entista-paremmin?publisherId=3747&releaseId=69902466  

    2021.02

  • The digital as fieldwork for historical researchers

    Internet

    Author： Other  

    https://journal.fi/ennenjanyt/article/view/108931/63923?acceptCookies=1  

    2018

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Academic Activities 【 display / non-display 】

  • Peer review for DHNB

    Peer review

    2021
    　
    　

  • Peer review for NoDaLiDa

    Peer review

    2019
    -
    2021

  • Peer review for DHNB

    Peer review

    2019
    -
    2021

  • Member of steering group for digital development at the University of Helsinki

    Other

    2018
    -
    2021

  • Peer review for EMNLP

    Peer review

    2020
    　
    　

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