Title :
Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining
Author :
Poria, S. ; Gelbukh, A. ; Hussain, Amir ; Howard, Newton ; Das, Divya ; Bandyopadhyay, Supriyo
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.
Keywords :
Internet; data mining; SenticNet 1.0; SenticNet concepts; affective information; affective labels; concept-based opinion mining; emotion label; Data mining; Emotion recognition; Feature extraction; Information analysis; Intelligent systems; Knowledge discovery; Natural language processing; Vocabulary; Data mining; Emotion recognition; Feature extraction; Information analysis; Intelligent systems; Knowledge discovery; Natural language processing; SenticNet; Vocabulary; WordNet-Affect; emotion lexicon; intelligent systems; opinion mining; sentic computing; sentiment analysis;
Journal_Title :
Intelligent Systems, IEEE
