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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
Authors: Poria, Soujanya
Gelbukh, Alexander
Hussain, Amir
Howard, Newton
Das, Dipankar
Bandyopadhyay, Sivaji
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Issue Date: Mar-2013
Publisher: IEEE
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.
Type: Journal Article
DOI Link:
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Jadavpur University
National Polytechnic Institute
Computing Science - CSM Dept
Massachusetts Institute of Technology
National Institute of Technology, India
Jadavpur University

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