Titlebook: Emotion Detection in Natural Language Processing; Federica Cavicchio Book 2025 The Editor(s) (if applicable) and The Author(s), under excl

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Theoretical Foundations of Emotions,xperiences such as affect, feelings, sentiment, and opinions. It highlights various models of emotion classification, specifically distinguishing between categorical and dimensional approaches. Categorical models of emotion classify emotions into distinct categories: happiness, sadness, anger, fear,

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Rule-Based Systems for Emotion Detection,ules and patterns derived from specialised lexicons and expertly annotated datasets, provide a structured and interpretable approach to identifying emotions in texts. By utilising lexicons and annotated datasets, researchers created models that could detect sentiment and emotional nuances with a pra

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Challenges and Ethical Considerations of Emotion Detection,gulatory standards for emotion and mood detection in applications used in sensitive fields such as healthcare. It explores how explainable AI can facilitate better understanding and trust among participants from diverse disciplines (e.g. psychology and healthcare), ensuring that AI systems are trans

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2511-2066 eotechnical remediation in connection with preventing seepage from reservoirs situated in evaporites, particularly in gypsum, it puts forward a new chemical solution that, after painstaking laboratory testing, 978-3-030-18523-7978-3-030-18521-3Series ISSN 2511-2066 Series E-ISSN 2511-2082

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