Sentic computing for patient centered applications

Cambria, Erik; Hussain, Amir; Durrani, Tariq; Havasi, Catherine; Eckl, Chris; Munro, James

Abstract

Next-generation patients are far from being peripheral to health-care. They are central to understanding the effectiveness and efficiency of services and how they can be improved. Today a lot of patients are used to reviewing local health services on-line but this social information is just stored in natural language text and it is not machine-accessible and machine-processable. To distil knowledge from this extremely unstructured information we use Sentic Computing, a new opinion mining and sentiment analysis paradigm which exploits AI and Semantic Web techniques to better recognize, interpret and process opinions and sentiments in natural language text. In particular, we use a language visualization and analysis system, a novel emotion categorization model, a resource for opinion mining based on a web ontology and novel techniques for finding and defining topic dependent concepts, namely spectral association and CF-IOF weighting respectively.

Citation

Cambria, E., Hussain, A., Durrani, T., Havasi, C., Eckl, C., & Munro, J. (2010, October). Sentic computing for patient centered applications. Presented at IEEE 10th International Conference on Signal Processing, Beijing, China

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: IEEE 10th International Conference on Signal Processing
• Start Date: Oct 24, 2010
• End Date: Oct 28, 2010
• Online Publication Date: Dec 3, 2010
• Publication Date: 2010
• Deposit Date: Sep 19, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1279-1282
• Series ISSN: 2164-523X
• Book Title: IEEE 10th International Conference on Signal Processing Proceedings
• ISBN: 9781424458974
• DOI: https://doi.org/10.1109/ICOSP.2010.5657072
• Keywords: AI, Semantic Networks, Knowledge Base Management, NLP, Opinion Mining and Sentiment Analysis, E-Health
• Public URL: http://researchrepository.napier.ac.uk/Output/1793444