Stacked LSTM Model for Contextual Correlation Detection Among Multiple Emotions
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Le résumé fourni par la source
Emotions are closely tied to human behavior and play a critical role in daily life. With the widespread use of social media, individuals frequently express their feelings online, making emotion extraction from social networks an active area of research. This has applications in domains such as patient emotion monitoring, emotional text-to-speech synthesis, and empathetic chatbots. While numerous studies have focused on single-emotion detection, limited work has addressed the simultaneous identification of multiple coexisting emotions. These emotions are often influenced by contextual factors, including past emotional states and social interactions. Leveraging such dependencies can significantly enhance emotion detection performance. This study proposes a fast, accurate, and robust method to predict future emotions based on current emotional states, while also identifying interrelated emotions expressed in social media posts. We annotate a public dataset with multi-label emotion tags and model contextual dependencies—both temporal and social—using a novel Stacked Long Short-Term Memory (LSTM) architecture. The model incorporates encoder-decoder layers to capture short- and long-term dependencies among emotions and employs a time-distributed dense layer to differentiate features associated with each emotion. Our experiments show significant improvements in capturing contextual correlations among multiple emotions. The proposed model achieved a micro F1-score of 0.50 on the GoEmotions dataset and demonstrated comparable performance to transformer-based models on the DepressionEmo dataset, while requiring fewer computational resources. These findings underscore the effectiveness and efficiency of our approach in modeling complex emotional dynamics across social networks.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Stacked LSTM Model for Contextual Correlation Detection Among Multiple Emotions
- Date Crossref
- 01/01/2025
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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National University of Computer and Emerging Sciences Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Government College Women University Faisalabad Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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University of Agriculture Faisalabad Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Macquarie University pays non établi dans la noticeUniversité ou école supérieure
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Kean University Department of Computer Science and Technology pays non établi dans la noticeUniversité ou école supérieure
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School of Computing pays non établi dans la noticeUniversité ou école supérieure
Department of Computer Science — National University of Computer and Emerging Sciences, Department of Computer Science — Government College Women University Faisalabad et Department of Computer Science — University of Agriculture Faisalabad, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.