Title :
Enriching SenticNet Polarity Scores through Semi-Supervised Fuzzy Clustering
Author :
Poria, S. ; Gelbukh, A. ; Cambria, Erik ; Das, Divya ; Bandyopadhyay, Supriyo
Author_Institution :
Comput. Sci. & Eng. Dept., Jadavpur Univ., Kolkata, India
Abstract :
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.
Keywords :
data mining; fuzzy set theory; learning (artificial intelligence); pattern clustering; text analysis; SenticNet 1.0; SenticNet polarity scores; WordNet-Affect emotion lists; concept-level opinion mining; emotion labels; emotion recognition; features extraction; lexical resources; semi-supervised fuzzy clustering; Accuracy; Clustering algorithms; Conferences; Feature extraction; Mutual information; Natural languages; Vectors; Fuzzy clustering; ISEAR dataset; Sentic computing; SenticNet; Sentiment analysis; WordNet; WordNet-Affect;
Conference_Titel :
Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on
Conference_Location :
Brussels
Print_ISBN :
978-1-4673-5164-5
DOI :
10.1109/ICDMW.2012.142
