Descrizione:
Emotion and Cause | Linguistic Theory and Computational Implementation | Sophia Yat Mei Lee | Buch | Studies in East Asian Linguistics | xii | Englisch | 2018 | Springer | EAN 9789811061929 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Codice articolo 111029724
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Riassunto:
This work argues that cause events, being the most tangible component of emotion, provide a rich dimension of how emotions should be classified. While it is often claimed that emotional concepts cannot be defined, this work views emotion as a response triggered by actual or perceived events, specifically focusing on the interaction between five primary emotions (Happiness, Sadness, Fear, Anger, and Surprise) and cause events. Cause events are examined in terms of two dimensions, namely transitivity and epistemicity. By incorporating the semantic and syntactic information of emotion cause events, this representation of emotion not only provides deep linguistic criteria of emotion cause events, but also offers an event-based approach to emotion classification. A text-driven, rule-based system for detecting the causes of emotion is then developed to establish the validity of the proposed linguistic model for emotion detection and classification. The system shows promising results.
Dalla quarta di copertina:
This work argues that cause events, being the most tangible component of emotion, provide a rich dimension of how emotions should be classified. While it is often claimed that emotional concepts cannot be defined, this work views emotion as a response triggered by actual or perceived events, specifically focusing on the interaction between five primary emotions (Happiness, Sadness, Fear, Anger, and Surprise) and cause events. Cause events are examined in terms of two dimensions, namely transitivity and epistemicity. By incorporating the semantic and syntactic information of emotion cause events, this representation of emotion not only provides deep linguistic criteria of emotion cause events, but also offers an event-based approach to emotion classification. A text-driven, rule-based system for detecting the causes of emotion is then developed to establish the validity of the proposed linguistic model for emotion detection and classification. The system shows promising results.
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