Year: 2024
Type: article
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-municationssuch as emails, chat messages, or social media posts-to identify emotional states, enabling timely interventions for those experiencing distress, anxiety, or depression.This technology can assist in monitoring patient progress, ensuring that healthcare providers can tailor their approaches based on realtime emotional feedback.Moreover, it can support telemedicine by providing context to patient narratives, enhancing remote diag-nostics and consultations.BiLSTMbased 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.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403074801