Natural Language Processing for Sentiment Analysis in mHealth Systems: Applications and Challenges
Abstract
This paper explores the application of natural language processing for sentiment analysis in mHealth systems. It discusses the challenges of implementing NLP models for text-based emotion detection and presents solutions to improve the accuracy of sentiment analysis in healthcare communications.
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Baltrušaitis, T., Zadeh, A., Lim, Y. C., & Morency, L. P. (2018). OpenFace 2.0: Facial behavior analysis toolkit. 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 59-66. https://doi.org/10.1109/FG.2018.00019
Calvo, R. A., D'Mello, S., Gratch, J., & Kappas, A. (2015). The Oxford handbook of affective computing. Oxford University Press.
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.
Wöllmer, M., Eyben, F., Schuller, B., & Rigoll, G. (2010). A multi-modal LSTM–MRF model for robust facial expression recognition. 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3642-3645. https://doi.org/10.1109/ICASSP.2010.5495407
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009). A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(1), 39-58. https://doi.org/10.1109/TPAMI.2008.52
Pillai, S. E. V. S., & Hu, W. C. (2023, May). Misinformation detection using an ensemble method with emphasis on sentiment and emotional analyses. In 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) (pp. 295-300). IEEE.
Kalla, D., Smith, N., Samaah, F., & Polimetla, K. (2022). Enhancing Early Diagnosis: Machine Learning Applications in Diabetes Prediction. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-205. DOI: doi. org/10.47363/JAICC/2022 (1), 191, 2-7.