オーマン エミリー (オーマン エミリー)

写真a

所属

国際学術院 国際教養学部

職名

講師(テニュアトラック)

学歴 【 表示 / 非表示

  • 2016年01月
    -
    2021年03月

    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年
    -
    2021年

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

  • 2016年07月
     
     

    Instituto Superior Técnico (IST)   Lisbon Machine Learning Summer School  

  •  
    -
    2015年10月

    Linnaeus University   Department of English   MA in English Linguistics  

経歴 【 表示 / 非表示

  • 2021年04月
    -
    継続中

    早稲田大学   国際教養学部   講師

  • 2020年12月
    -
    2021年03月

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

  • 2020年09月
    -
    2021年03月

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

    Group grant

  • 2016年01月
    -
    2021年03月

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

  • 2014年09月
    -
    2015年12月

    University of Helsinki   Department of English   Language Change Database   Research Assistant

全件表示 >>

所属学協会 【 表示 / 非表示

  • 2021年
    -
    継続中

    Japanese Association for Digital Humanities

  • 2020年
    -
    継続中

    European Association for Digital Humanities

  • 2018年
    -
    継続中

    Rajapinta ry (Computational Social Science)

  • 2018年
    -
    継続中

    Digital Humanities in the Nordic Countries

 

研究分野 【 表示 / 非表示

  • 科学教育

  • 図書館情報学、人文社会情報学

  • 高等教育学

  • 言語学

  • 知能ロボティクス

全件表示 >>

研究キーワード 【 表示 / 非表示

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

論文 【 表示 / 非表示

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

    Emily Öhman, Marc Pàmies, Kaisla Kajava, Jörg Tiedemann

        6542 - 6552  2020年  [査読有り]

    担当区分:筆頭著者, 責任著者

    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年  [査読有り]

    担当区分:筆頭著者, 責任著者

    DOI

  • SELF & FEIL

    Öhman Emily

       2021年04月

    担当区分:筆頭著者, 責任著者

     概要を見る

    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年03月  [査読有り]  [国際誌]

    担当区分:筆頭著者, 責任著者

     概要を見る

    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年  [査読有り]

    担当区分:筆頭著者, 責任著者

     概要を見る

    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.

全件表示 >>

Works(作品等) 【 表示 / 非表示

  • FEIL (Finnish Emotion Intensity Lexicon)

    Emily Ohman  その他 

    2020年
    -
    2021年

  • SELF (Sentiment and Emotion Lexicon for Finnish)

    Emily Öhman  その他 

    2020年
    -
    2021年

  • XED multilingual emotion-annotated dataset

    Emily Ohman, Kaisla Kajava, Marc Pàmies, Jörg Tiedemann  データベース 

    2020年
    -
    2021年

  • Sentimentator (dockerized sentiment annotation tool)

    Emily Ohman, Kaisla Kajava  ソフトウェア 

    2018年
     
     

受賞 【 表示 / 非表示

  • Small grants

    2020年11月   European Association for Digital Humanities   Python for digital humanities  

  • Future Digileader

    2020年11月   KTH Royal Institute of Technology, Digital Futures research center, Sweden   Future Digileader  

共同研究・競争的資金等の研究課題 【 表示 / 非表示

  • Unconventional Communicators in the COVID crisis

    研究期間:

    2020年
    -
    2022年
     

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

    担当区分: 研究分担者

  • Intimacy in Data-driven Culture

    研究期間:

    2020年
    -
    2021年03月
     

    Anu Koivunen, Kaarina Nikunen

    担当区分: 連携研究者

  • 博士

    研究期間:

    2016年01月
    -
    2020年11月
     

    担当区分: 研究代表者

講演・口頭発表等 【 表示 / 非表示

  • What to expect from an academic career?

    Maryam Elahi, Naveen Bagalkot, Emily Ohman  [招待有り]

    Future Digileaders  

    発表年月: 2021年10月

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

    Amy Grace Metcalfe, Emily Ohman

    Japanese Association for Digital Humanities Conference 2021  

    発表年月: 2021年09月

  • 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  [招待有り]

    European Association for Digital Humanities  

    発表年月: 2021年09月

  • AI Ethics and Applications in Finance

    Emily Ohman  [招待有り]

    INDEX Varainhoito  

    発表年月: 2021年09月

  • AI for the Environment

    Emily Ohman  [招待有り]

    Finnish Environment Institute  

    発表年月: 2021年02月

全件表示 >>

 

現在担当している科目 【 表示 / 非表示

全件表示 >>

担当経験のある科目(授業) 【 表示 / 非表示

  • -

    早稲田大学  

    2021年09月
    -
    継続中
     

  • Pythonプログラミング

    早稲田大学  

    2021年04月
    -
    継続中
     

  • デジテルヒュマニティーズ入門

    早稲田大学  

    2021年04月
    -
    継続中
     

  • 無し

    早稲田大学  

    2021年04月
    -
    継続中
     

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

    University of Helsinki  

    2021年01月
    -
    2021年04月
     

全件表示 >>

 

委員歴 【 表示 / 非表示

  • 2021年04月
    -
    継続中

    早稲田大学  統計学教育検討委員会

  •  
     
     

    University of Helsinki  Ethics committee

  •  
     
     

    University of Helsinki  Teaching Evaluation Committee

メディア報道 【 表示 / 非表示

  • Lectio praecursoria: The Language of Emotions

    インターネットメディア

    Rajapinta ry. (Computational social science academic society)  

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

    2021年04月

  • Uusilla metodeilla voi analysoida tekstin tunnelatauksia entistä paremmin

    インターネットメディア

    執筆者: 本人以外  

    Finnish National News Agency (STT)  

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

    2021年02月

  • The digital as fieldwork for historical researchers

    インターネットメディア

    執筆者: 本人以外  

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

    2018年

学術貢献活動 【 表示 / 非表示

  • Peer review for NoDaLiDa

    査読等

    2019年
    -
    2021年
  • Peer review for DHNB

    査読等

    2019年
    -
    2021年
  • Member of steering group for digital development at the University of Helsinki

    その他

    2018年
    -
    2021年
  • Peer review for EMNLP

    査読等

    2020年
     
     
  • Peer review for EACL

    査読等

    2020年
     
     

全件表示 >>