Enhanced SenticNet with affective labels for concept-based opinion mining

Poria, Soujanya; Gelbukh, Alexander; Hussain, Amir; Howard, Newton; Das, Dipankar; Bandyopadhyay, Sivaji

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.

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), 2-9. https://doi.org/10.1109/MIS.2013.4

• Journal Article Type: Article
• Online Publication Date: Jan 21, 2013
• Publication Date: 2013-04
• Deposit Date: Oct 11, 2019
• Journal: IEEE Intelligent Systems
• Print ISSN: 1541-1672
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 28
• Issue: 2
• Pages: 2-9
• DOI: https://doi.org/10.1109/MIS.2013.4
• Keywords: 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, WordNet-Affect
• Public URL: http://researchrepository.napier.ac.uk/Output/1793144