Sentiment Detection in Mobile Health Chatbots: AI and NLP Solutions

Prof. Arjun Singhal

Abstract


This study proposes a sentiment detection framework for mobile health chatbots using AI and natural language processing. By analyzing patient queries and responses, the system detects emotional states, allowing chatbots to provide empathetic support for mental health issues.

References


Pillai, S. E. V. S., ElSaid, A. A., & Hu, W. C. (2022, May). A Self-Reconfigurable System for Mobile Health Text Misinformation Detection. In 2022 IEEE International Conference on Electro Information Technology (eIT) (pp. 242-247). IEEE.

Corcoran, P., & Carr, D. (2019). AI in the detection of emotion in facial expressions. IEEE Transactions on Consumer Electronics, 65(1), 75-83. https://doi.org/10.1109/TCE.2019.2892218

Ekman, P., & Friesen, W. V. (2003). Unmasking the face: A guide to recognizing emotions from facial expressions. Malor Books.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Hinton, G., & Salakhutdinov, R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507. https://doi.org/10.1126/science.1127647

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

Ko, B. C. (2018). A brief review of facial emotion recognition based on visual information. Sensors, 18(2), 401. https://doi.org/10.3390/s18020401

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539

Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. Mining Text Data, 415-463. https://doi.org/10.1007/978-1-4614-3223-4_13

McDuff, D., & El Kaliouby, R. (2015). Applications of automatic facial coding in media measurement. IEEE Transactions on Affective Computing, 6(2), 190-202. https://doi.org/10.1109/TAFFC.2015.2445334

Mehrabian, A. (1971). Silent messages: Implicit communication of emotions and attitudes. Wadsworth Publishing Company.

Mittal, T., Bhattacharya, U., Chandra, R., Bera, A., & Manocha, D. (2020). EmotiCon: Context-aware multimodal emotion recognition using frege's principle. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14234-14243. https://doi.org/10.1109/CVPR42600.2020.01425

Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017). A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 37, 98-125. https://doi.org/10.1016/j.inffus.2017.02.003

Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161-1178. https://doi.org/10.1037/h0077714

Scherer, K. R., Bänziger, T., & Roesch, E. B. (2010). A blueprint for affective computing: A sourcebook and manual. Oxford University Press.

Shen, L., Wang, M., & Shen, Y. (2011). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, 38(10), 14059-14065. https://doi.org/10.1016/j.eswa.2011.04.066

Tkalčič, M., De Carolis, B., De Gemmis, M., Odić, A., & Košir, A. (2016). Emotions and personality in personalized services. Springer.

Kalla, D., Smith, N., Samaah, F., & Polimetla, K. (2021). Facial Emotion and Sentiment Detection Using Convolutional Neural Network. Indian Journal of Artificial Intelligence Research (INDJAIR), 1(1), 1-13.


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