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2012 IEEE 12th International Conference on Data Mining Workshops
Enriching SenticNet Polarity Scores through Semi-Supervised Fuzzy Clustering
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
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.
Index Terms:
Feature extraction,Vectors,Accuracy,Clustering algorithms,Natural languages,Mutual information,Conferences,Fuzzy clustering,SenticNet,Sentic computing,Sentiment analysis,WordNet,WordNet-Affect,ISEAR dataset
Citation:
Soujanya Poria, Alexander Gelbukh, Erik Cambria, Dipankar Das, Sivaji Bandyopadhyay, "Enriching SenticNet Polarity Scores through Semi-Supervised Fuzzy Clustering," icdmw, pp.709-716, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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