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SenseEmo.ai: Deep Learning-based Textual Human Emotion Recognition
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 38 |
| Year of Publication: 2024 |
| Authors: Shibakali Gupta, Arpan Kundu, Siddhanta Debnath, Pritam Roy Chowdhury |
10.5120/ijca2024923961
|
Shibakali Gupta, Arpan Kundu, Siddhanta Debnath, Pritam Roy Chowdhury . SenseEmo.ai: Deep Learning-based Textual Human Emotion Recognition. International Journal of Computer Applications. 186, 38 ( Sep 2024), 41-46. DOI=10.5120/ijca2024923961
Abstract
Text-based emotion detection using Bidirectional Long Short-Term Memory (BiLSTM) networks represents a significant advance-ment in natural language processing, particularly in healthcare ap-plications. This method leverages the capabilities of LSTM net-works to capture temporal dependencies in textual data, while the bidirectional approach allows the model to understand con-text from both past and future states, enhancing its ability to dis-cern subtle emotional cues. In healthcare, accurate emotion de-tection can greatly improve patient care and mental health sup-port. For instance, automated systems can analyze patient com-munications—such as emails, chat messages, or social media posts—to identify emotional states, enabling timely interventions for those experiencing distress, anxiety, or depression. This tech-nology can assist in monitoring patient progress, ensuring that healthcare providers can tailor their approaches based on real-time emotional feedback. Moreover, it can support telemedicine by providing context to patient narratives, enhancing remote diag-nostics and consultations. BiLSTM-based emotion detection can also be integrated into virtual therapy platforms, offering ther-apists insights into a patient’s emotional well-being over time. This application not only improves therapeutic outcomes but also makes mental health support more accessible and responsive. Overall, the implementation of BiLSTM in emotion detection fosters a more empathetic and effective healthcare environment.
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