Research Output

Sentic computing for patient centered applications

  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.

  • Date:

    03 December 2010

  • Publication Status:

    Published

  • Publisher

    IEEE

  • DOI:

    10.1109/ICOSP.2010.5657072

  • Funders:

    Historic Funder (pre-Worktribe)

Citation

Cambria, E., Hussain, A., Durrani, T., Havasi, C., Eckl, C., & Munro, J. (2010). Sentic computing for patient centered applications. In IEEE 10th International Conference on Signal Processing Proceedings, 1279-1282. https://doi.org/10.1109/ICOSP.2010.5657072

Authors

Keywords

AI, Semantic Networks, Knowledge Base Management, NLP, Opinion Mining and Sentiment Analysis, E-Health

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