Research Article

SenseEmo.ai: Deep Learning-Based Textual Human Emotion Recognition

by  Shibakali Gupta, Arpan Kundu, Siddhanta Debnath, Pritam Roy Chowdhury
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Issue 38
Published: September 2024
Authors: Shibakali Gupta, Arpan Kundu, Siddhanta Debnath, Pritam Roy Chowdhury
10.5120/ijca2024923961
PDF

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 (September 2024), 41-46. DOI=10.5120/ijca2024923961

                        @article{ 10.5120/ijca2024923961,
                        author  = { Shibakali Gupta,Arpan Kundu,Siddhanta Debnath,Pritam Roy Chowdhury },
                        title   = { SenseEmo.ai: Deep Learning-Based Textual Human Emotion Recognition },
                        journal = { International Journal of Computer Applications },
                        year    = { 2024 },
                        volume  = { 186 },
                        number  = { 38 },
                        pages   = { 41-46 },
                        doi     = { 10.5120/ijca2024923961 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2024
                        %A Shibakali Gupta
                        %A Arpan Kundu
                        %A Siddhanta Debnath
                        %A Pritam Roy Chowdhury
                        %T SenseEmo.ai: Deep Learning-Based Textual Human Emotion Recognition%T 
                        %J International Journal of Computer Applications
                        %V 186
                        %N 38
                        %P 41-46
                        %R 10.5120/ijca2024923961
                        %I Foundation of Computer Science (FCS), NY, USA
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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Index Terms
Computer Science
Information Sciences
Text-Based Emotion Detection
Deep learning
Patient Communications
Anxiety
Depression
AI in Healthcare
Keywords

Bidirectional Long Short-Term Memory (BiLSTM) Natural Lan-guage Processing (NLP) Temporal Dependencies Contextual Understanding Mental Health Support Automated Systems Patient Communications

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