Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDMW.2012.142
Title: Enriching SenticNet polarity scores through semi-supervised fuzzy clustering
Authors: Poria, S.
Gelbukh, A.
Cambria, E. 
Das, D.
Bandyopadhyay, S.
Keywords: Fuzzy clustering
ISEAR dataset
Sentic computing
SenticNet
Sentiment analysis
WordNet
WordNet-Affect
Issue Date: 2012
Citation: Poria, S., Gelbukh, A., Cambria, E., Das, D., Bandyopadhyay, S. (2012). Enriching SenticNet polarity scores through semi-supervised fuzzy clustering. Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012 : 709-716. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDMW.2012.142
Abstract: SenticNet 1.0 is one of the most widely used freely-available resources for concept-level opinion mining, containing about 5,700 common sense concepts and their corresponding polarity scores. Specific affective information associated to such concepts, however, is often desirable for tasks such as emotion recognition. In this work, we propose a method for assigning emotion labels to SenticNet concepts based on a semi-supervised classifier trained on WordNet-Affect emotion lists with features extracted from various lexical resources. © 2012 IEEE.
Source Title: Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/116072
ISBN: 9780769549255
DOI: 10.1109/ICDMW.2012.142
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