TY - GEN
T1 - Sentic computing for patient centric applications
AU - Cambria, Eric
AU - Hussain, Amir
AU - Durrani, Tariq
AU - Havasi, C
AU - Eckl, C
AU - Munro, J
N1 - [1] BBC–Panorama investigation: Trust Us, We’re an NHS Hospital –
http://news.bbc.co.uk/2/hi/uk news/8551668.stm
[2] E. Cambria, A. Hussain, C. Havasi, and C. Eckl. Sentic Computing:
Exploitation of Common Sense for the Development of Emotion-Sensitive
Systems. LNCS, vol. 5967, pp. 148–156. Springer–Verlag, Berlin Heidelberg (2010)
[3] E. Cambria, A. Hussain, C. Havasi, and C. Eckl. AffectiveSpace: Blending
Common Sense and Affective Knowledge to Perform Emotive Reasoning.
In: WOMSA at CAEPIA09, Seville (2009)
[4] C. Strapparava, and A. Valitutti: WordNet-Affect: an Affective Extension
of WordNet. In: LREC, Lisbon (2004)
[5] C. Havasi, R. Speer, and J. Alonso: ConceptNet 3: a Flexible, Multilingual
Semantic Network for Common Sense Knowledge. In: RANLP, Borovets
(2007)
[6] R. Speer, C. Havasi, and H. Lieberman: AnalogySpace: Reducing the
Dimensionality of Common Sense Knowledge. In: AAAI, Chicago (2008)
[7] E. Cambria, A. Hussain, C. Havasi, and C. Eckl. Common Sense Computing: From the Society of Mind to Digital Intuition and Beyond. LNCS,
vol. 5707, pp. 252–259. Springer–Verlag, Berlin Heidelberg (2009)
[8] R. Plutchik. The Nature of Emotions. American Scientist 89(4), 344–350
(2001)
[9] M. Minsky. The Emotion Machine. Simon and Schuster, New York (2006)
[10] E. Cambria, R. Speer, C. Havasi, and A. Hussain. SenticNet: a Publicly
Available Semantic Resource for Opinion Mining. In: AAAI CSK10,
Arlington (2010)
[11] C. Havasi, R. Speer, and J. Holmgren. Automated Color Selection Using
Semantic Knowledge. In: AAAI CSK10, Arlington (2010)
[12] E. Cambria, A. Hussain, C. Havasi, C. Eckl and J. Munro: Towards
Crowd Validation of the UK National Health Service. In: WebSci10,
Raleigh (2010)
[13] Patient Opinion – http://www.patientopinion.org.uk
[14] NHS Choices – http://www.nhs.uk
PY - 2010/10
Y1 - 2010/10
N2 - 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.
AB - 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.
KW - sentic computing
KW - patient centric applications
KW - semantics
KW - XML
KW - analytical models
KW - approximation methods
KW - databases
KW - hospitals
KW - natural languages
U2 - 10.1109/ICOSP.2010.5657072
DO - 10.1109/ICOSP.2010.5657072
M3 - Conference contribution book
SN - 9781424458974
SP - 1279
EP - 1282
BT - 2010 IEEE 10th international conference on signal processing (ICSP)
PB - IEEE
CY - New York
T2 - 10th IEEE International Conference on Signal Processing
Y2 - 24 October 2010 through 28 October 2010
ER -