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.

References

• M.Balaji, Dr. N. Yuvaraj Intelligent Chatbot Model to Enhance the Emotion Detection in social media using Bi-directional Recur-rent Neural Network
• Aishwarya B R , Deepthi P , Greeshma B G , Madhumitha Raj C and Jahnavi S Sentiment Analysis for chatbots to make them emotionally reactive
• Rajani S Kamath,Shruti Jamsandekar and Mr. M.B.Patil Chat-bot Response Mining usingSentiment Analysis
• Pinkesh Badjatiya , Shashank Gupta , Manish Gupta and Va-sudeva Varma Deep Learning for Hate Speech Detection in Tweets
• Prima Widyaningrum, Yova Ruldeviyani, Ramanti Dharayani Sentiment Analysis to Assess the Community’s Enthusiasm to-wards the Development Chatbot Using an Appraisal Theory
• Xing Fang and Justin Zhan Sentiment analysis using product review data
• Zeyad Khalid — Emotion Recognition LSTM 93 per-cent,Kaggle
• Kerstin Denecke , Nicole Schmid , Stephan N¨ussli Deep Learning Based Chatbot Framework for Mental Health Therapy, 2022, SPRINGER
• Mariappan, Archana, Preliminary identification and therapeu-tic support of depression in mental health using conversational AI,2023
• Salah Hamdoun, Terri Bookman, Katina Michael AI-Based and Digital Mental Health Apps: Balancing Need and Risk,2023,IEEE.
• Aadil Ganie, Samad Dadvandipour Social Media Posts as a Window into Mental Health: A Machine Learning Ap-proach,2023,Dimensions.
• Yuhao He, Li Yang, Xiaokun Zhu,Bin Wu, Shuo Zhang, Chunlian Qian, Tian Tian Mental Health Chatbot for Young Adults With Depressive Symptoms During The COVID-19 Pandemic: Single-Blind, Three-Arm Randomized Controlled Trial, 2022,NIH
• Jordan J. Bird, Anik´o Ek´art, Diego R. Faria Chatbot In-teraction with Artificial Intelligence: human data augmentation with T5 and language transformer ensemble for text classifica-tion,2021,SPRINGER
• Payam Kaywan, Khandakar Ahmed, Ayman Ibaida, Yuan Miao,Bruce Gu Early detection of depression using a conversa-tional AI bot: A non- clinical trial, 2023, GOOGLE SCHOLAR
• Hyojin Chin, Gabriel Lima,Mingi Shin, Assem Zhunis, Chiy-oung Cha, Junghoi Choi, Meeyoung Cha User-Chatbot Conversa-tions During the COVID-19 Pandemic: Study Based on Topic Mod-eling and Sentiment Analysis,2023,JMIR PUBLICATION
• Wagner da Silva Maciel Sodre´, Julio Cesar Duarte Chatbot Optimiza- tion using Sentiment Analysis and Timeline Navigation, 2023
• Machov´a, K., Mikula, M., Gao, X., and Mach, M. (2020a) Lexicon- based sentiment analysis using the particle swarm opti-mization
• Sailunaz, K., Dhaliwal, M., Rokne, J., and Alhajj, R. (2018). Emotion detection from text and speech: a survey.
• Szab´oov´a, M., Sarnovsk´y, M., Maslej-Kre•s•n´akov´a, V., and Machov´a, K.(2020) Emotion analysis in human-robot in-teraction.
• Chatterjee, A., Gupta, U., Chinnakotla, M. K., Srikanth, R., Galley, M., and Agrawal, P. (2019). Understanding emotions in text using deep learning and big data.
• Kratzwald, B., Ili´c, S., Kraus, M., Feuerriegel, S., and Prendinger, H. (2018). Deep learning for affective computing: text-based emotion recognition in decision support
• Daniel Y. Park , and Hyungsook Kim Determinants of In-tentions to Use Digital Mental Healthcare Content among Univer-sity Students, Faculty, and Staff: Motivation, Perceived Usefulness, Perceived Ease of Use, and Parasocial Interaction with AI Chatbot, 2022, SPRINGER
• Nivedita Bhirud, Subhash Tatale, Priyanka Jain, Archis Kulkarni, Mayuresh Panchpor, N. K. Jain Development of Psy-chiatric COVID-19 CHATBOT Using Deep Learning, 2022, SPRINGER
• Bhargavaram Muppuru, Sannareddy Vamsi, J. Jayashree, Leburu Sohan, Dhiran Sai Krishna REAL TIME SENTIMEN-TAL ANALYSIS OF CHAT DATA USING DEEP LEARN-ING,2023,Journal of Data Acquisition and Processing.
• Wagner da Silva Maciel Sodr ´e, Julio Cesar Duarte Chatbot Optimiza- tion using Sentiment Analysis and Timeline Navigation, 2023, Research- Gate