Sentic computing for patient centric applications

Eric Cambria, Amir Hussain, Tariq Durrani, C Havasi, C Eckl, J Munro

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

59 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2010 IEEE 10th international conference on signal processing (ICSP)
Place of PublicationNew York
PublisherIEEE
Pages1279-1282
Number of pages4
ISBN (Print)9781424458974
DOIs
Publication statusPublished - Oct 2010
Event10th IEEE International Conference on Signal Processing - Beijing, China
Duration: 24 Oct 201028 Oct 2010

Conference

Conference10th IEEE International Conference on Signal Processing
CountryChina
CityBeijing
Period24/10/1028/10/10

Keywords

  • sentic computing
  • patient centric applications
  • semantics
  • XML
  • analytical models
  • approximation methods
  • databases
  • hospitals
  • natural languages

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  • Cite this

    Cambria, E., Hussain, A., Durrani, T., Havasi, C., Eckl, C., & Munro, J. (2010). Sentic computing for patient centric applications. In 2010 IEEE 10th international conference on signal processing (ICSP) (pp. 1279-1282). IEEE. https://doi.org/10.1109/ICOSP.2010.5657072