Browsing by Subject "language Technology / Digital Humanities"

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  • Öhman, Emily (Helsingin yliopisto, 2021)
    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 light-weight 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.