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

fYear

2012

fDate

10-10 Dec. 2012

Firstpage

709

Lastpage

716

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on

Conference_Location

Brussels

Print_ISBN

978-1-4673-5164-5

Type

conf

DOI

10.1109/ICDMW.2012.142

Filename

6406509