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May-June 2013 (vol. 28 no. 3)
pp. 6-9
Erik Cambria, National University of Singapore
Bjorn Schuller, Technische Universität München
Bing Liu, University of Illinois at Chicago
Haixun Wang, Microsoft Research Asia
Catherine Havasi, Massachusetts Institute of Technology
The guest editors introduce novel statistical approaches to concept-level sentiment analysis that go beyond a mere syntactic-driven analysis of text and provide semantic-based methods. Such approaches allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.
Index Terms:
Special issues and sections,Statistical analysis,Data mining,Computational linguistics,Social network services,Knowledge discovery,Text analysis,Natural language processing,intelligent systems,Special issues and sections,Statistical analysis,Data mining,Computational linguistics,Social network services,Knowledge discovery,Text analysis,Natural language processing,online social data,concept-level sentiment analysis,knowledge mining,data mining,opinion mining
Citation:
Erik Cambria, Bjorn Schuller, Bing Liu, Haixun Wang, Catherine Havasi, "Statistical Approaches to Concept-Level Sentiment Analysis," IEEE Intelligent Systems, vol. 28, no. 3, pp. 6-9, May-June 2013, doi:10.1109/MIS.2013.68
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