Stacked LSTM Model for Contextual Correlation Detection Among Multiple Emotions

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.