How to Cite?
Dr. Muddada Murali Krishna, Vankara Jayavani, Pooja Gotety, "Sentiment Classification in medical Care with Psychometric Analysis Using Emotion Detection," International Journal of Computer Trends and Technology, vol. 68, no. 3, pp. 63-66, 2020. Crossref, 10.14445/22312803/IJCTT-V68I3P112
The advancement of the technology in the present era is accelerating exponentially which leads to an increase in peer competition, mental tension and different mental problems like depression, schizophrenia, different disorders etc. So, a need for psychometric analysis is felt. Emotion recognition and sentiment analysis has gained a high level of popularity in research in the social networking but they have not been applied to the complicated problems of healthcare. But candidly speaking both the domains have great potential in solving some complex and interesting problems in medical science and engineering technology. This paper introduces a data science application, which acts as a psychometric analysis using the concept of Emotion recognition and sentiment analysis.
Emotion Recognition, Sentiment Analysis, Schizophrenia, Natural Language Processing and Machine Learning.
 M. Dooley and C. Dickinson, “The microbiology of cut-away peat,” Plant and Soil, vol. 32, no. 1-3, pp. 454–467, 1970.
 Vij and J. Pruthi, “An automated psychometric analyzer based on sentiment analysis and emotion recognition for healthcare,” Procedia Computer Science, vol. 132, pp. 1184–1191, 2018.
 K. Denecke and Y. Deng, “Sentiment analysis in medical settings: New opportunities and challenges,” Artificial intelligence in medicine, vol. 64, no. 1,pp. 17–27, 2015.
 H. S. Kisan, H. A. Kisan, and A. P. Suresh, “Collective intelligence & sentimental analysis of twitter data by using standford nlp libraries with software as a service (saas),” in Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference on, pp. 1–4, IEEE, 2016.
 T. D. Nguyen, L. D.-P. Nguyen, and T. Cao, “Sentiment analysis on medical text using combination of machine learning and so-cal scoring,” in Intelligent and Evolutionary Systems (IES), 2017 21st Asia Pacific Symposium on, pp. 49–54, IEEE, 2017.
 S. Casale, A. Russo, G. Scebba, and S. Serrano, “Speech emotion classification using machine learning algorithms,” in The IEEE International Conference on Semantic Computing, pp. 158–165, IEEE, 2008.
 P. Barlas, S. Adam, C. Chatelain, and T. Paquet, “A typed and handwritten text block segmentation system for heterogeneous and complex documents,” in Document Analysis Systems (DAS), 2014 11th IAPR International Workshop on, pp. 46–50, IEEE, 2014.
 G. Hales, “Visualisation of device datasets to assist digital forensic investigation,” in Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA), 2017 International Conference On, pp. 1–4, IEEE, 2017.
 P. Shahana and B. Omman, “Evaluation of features on sentimental analysis,” Procedia Computer Science, vol. 46, pp.1585–1592, 2015.
 G. K. Berdibaeva, O. N. Bodin, V. V. Kozlov, D. I. Nefed’ev, K. A. Ozhikenov, and Y. A. Pizhonkov, “Pre-processing voice signals for voice recognition systems,” in Micro/Nanotechnologies and Electron Devices (EDM), 2017 18th International Conference of Young Specialists on, pp. 242–245, IEEE, 2017.
 K. Duretec, A. Rauber, and C. Becker, “A text extraction software benchmark based on a synthesized dataset,” in Proceedings of the 17th ACM/IEEE Joint Conference on Digital Libraries, pp. 109–118, IEEE Press, 2017.
 F. Ibraheem and M. Z. Hussain, “Visualization of constrained data using trigonometric splines,” in Information Visualisation (IV), 2017 21st International Conference, pp. 400–404, IEEE, 2017.
 B. Gatos, N. Stamatopoulos, and G. Louloudis, “Icdar2009 handwriting segmentation contest,” International Journal on Document Analysis and Recognition (IJDAR), vol. 14, no. 1, pp. 25–33, 2011.
 A. Sarker, D. Molla´-Aliod, C. Paris, et al., “Outcome polarity identification of medical papers,” 2011.
 K. Saeed and M. Albakoor, “Region growing based segmentation algorithm for typewritten and handwritten text recognition,” Applied Soft Computing, vol. 9, no. 2, pp. 608–617, 2009.
 Z. Shi, S. Setlur, and V. Govindaraju,“A steerable directional local profile technique for extraction of handwritten arabic text lines,” in Document Analysis and Recognition, 2009. ICDAR’09. 10th International Conference on, pp. 176–180, IEEE, 2009.
 M. Soua, A. Benchekroun, R. Kachouri, and M. Akil, “Real-time text extraction based on the page layout analysis system,” in Real-Time Image and Video Processing 2017, vol. 10223, p.1022305, International Society for Optics and Photonics, 2017.
 N. Stamatopoulos, G. Louloudis, and B. Gatos, “Efficient transcript mapping to ease the creation of document image segmentation ground truth with text-image alignment,” in Frontiers in Handwriting Recognition (ICFHR), 2010 International Conference on, pp. 226– 231, IEEE, 2010.
 S. Tokuno, G. Tsumatori, S. Shono, E.Takei, T. Yamamoto, G. Suzuki, S. Mituyoshi, and M. Shimura, “Usage of emotion recognition in military health care,” in 2011 Defense Science Research Conference and Expo (DSR), pp. 1–5, IEEE, 2011.
 G. Louloudis, B. Gatos, I. Pratikakis, and C. Halatsis, “Text line and word segmentation of handwritten documents,” Pattern Recognition, vol.42, no. 12, pp. 3169–3183, 2009.