The paper "Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions" by Flor Miriam Plaza-del-Arco, Alba Curry, Amanda Cercas Curry, and Dirk Hovy provides a comprehensive review of 154 relevant NLP publications from the last decade. The authors address four key questions: how EA tasks are defined in NLP, prominent emotion frameworks and modeled emotions, the consideration of subjectivity in demographics and cultural factors, and primary NLP applications for EA. They identify several gaps in the field, including the lack of demographic and cultural aspects, poor fit of emotion categories to tasks, lack of standardized terminology, and insufficient interdisciplinary research. The paper also discusses the diverse range of emotion applications, with a focus on dialogue emotion recognition. The authors propose future directions, emphasizing the need for more diverse datasets, tailored emotion categories, standardized nomenclature, and interdisciplinary collaboration. They conclude with recommendations for future research to enhance the field of emotion analysis in NLP.The paper "Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions" by Flor Miriam Plaza-del-Arco, Alba Curry, Amanda Cercas Curry, and Dirk Hovy provides a comprehensive review of 154 relevant NLP publications from the last decade. The authors address four key questions: how EA tasks are defined in NLP, prominent emotion frameworks and modeled emotions, the consideration of subjectivity in demographics and cultural factors, and primary NLP applications for EA. They identify several gaps in the field, including the lack of demographic and cultural aspects, poor fit of emotion categories to tasks, lack of standardized terminology, and insufficient interdisciplinary research. The paper also discusses the diverse range of emotion applications, with a focus on dialogue emotion recognition. The authors propose future directions, emphasizing the need for more diverse datasets, tailored emotion categories, standardized nomenclature, and interdisciplinary collaboration. They conclude with recommendations for future research to enhance the field of emotion analysis in NLP.