DeepOpTED: An intelligent deep operator network for text emotion recognition

Emotion detection from textual data is a key challenge in natural language processing (NLP), playing an important role in applications such as sentiment analysis, human-computer interaction, and psychological evaluation. Growing utilization of social networks and online portals leads to the creation of a huge amount of reviews and ratings. Analyzing users’ and customers’ reviews and opinions are so important for governments and businesses. While recent advances have primarily leveraged transformer-based architectures for this task, we propose a novel approach by employing Deep Operator Networks (DeepONets), originally designed for learning operators in scientific computing, to model the mapping between textual representations and emotional states. In this study, we extract high-dimensional semantic embeddings from text using a pre-trained sentence transformer model and feed these embeddings into a DeepONet architecture for emotion classification. The primary contribution of this work lies in architectural innovation. To the best of our knowledge, this is the first application of DeepONet to textual emotion recognition, introducing a fundamentally different perspective on function approximation in language understanding tasks. Results obtained from experiments on benchmark emotion-labeled datasets indicate that our proposed model attains performance and results comparable to related baselines, with notable generalization capabilities across emotion categories. To ensure a fair and comprehensive evaluation, we assessed the performance of the proposed model using widely adopted classification metrics, including accuracy, precision, recall, and F1-score. We used two datasets and the results were around 80 percent on one dataset for all named metrics, and around 88 percent on the other. The findings suggest that DeepONet can serve as a robust alternative framework for capturing and modeling complex relationships in natural language processing tasks and opens new avenues for operator-based learning in text analysis.