Sentiment Detection in Mobile Health Chatbots: AI and NLP Solutions
Abstract
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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