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Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining
March-April 2013 (vol. 28 no. 2)
pp. 31-38
Soujanya Poria, Jadavpur University
Alexander Gelbukh, Instituto Politécnico Nacional
Amir Hussain, University of Stirling
Newton Howard, Massachusetts Institute of Technology
Dipankar Das, National Institute of Technology
Sivaji Bandyopadhyay, Jadavpur University
SenticNet 1.0 is one of the most widely used, publicly available resources for concept-based opinion mining. The presented methodology enriches SenticNet concepts with affective information by assigning an emotion label.
Index Terms:
Data mining,Knowledge discovery,Emotion recognition,Intelligent systems,Vocabulary,Feature extraction,Information analysis,Natural language processing,WordNet-Affect,Data mining,Knowledge discovery,Emotion recognition,Intelligent systems,Vocabulary,Feature extraction,Information analysis,Natural language processing,intelligent systems,SenticNet,sentic computing,sentiment analysis,opinion mining,emotion lexicon
Citation:
Soujanya Poria, Alexander Gelbukh, Amir Hussain, Newton Howard, Dipankar Das, Sivaji Bandyopadhyay, "Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining," IEEE Intelligent Systems, vol. 28, no. 2, pp. 31-38, March-April 2013, doi:10.1109/MIS.2013.4
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