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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Enhanced SenticNet with affective labels for concept-based opinion mining
Author(s): Poria, Soujanya
Gelbukh, Alexander
Hussain, Amir
Howard, Newton
Das, Dipankar
Bandyopadhyay, Sivaji
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Issue Date: Mar-2013
Citation: Poria S, Gelbukh A, Hussain A, Howard N, Das D & Bandyopadhyay S (2013) Enhanced SenticNet with affective labels for concept-based opinion mining, IEEE Intelligent Systems, 28 (2), pp. 31-38.
Abstract: 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.
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