Title :
Merging SenticNet and WordNet-Affect emotion lists for sentiment analysis
Author :
Poria, S. ; Gelbukh, A. ; Cambria, Erik ; PeiPei Yang ; Hussain, Amir ; Durrani, Tariq S.
Author_Institution :
Comput. Sci. & Eng. Dept., Jadavpur Univ., Kolkata, India
Abstract :
SenticNet is currently one of the most comprehensive freely available semantic resources for opinion mining. However, it only provides numerical polarity scores, while more detailed sentiment-related information for its concepts is often desirable. Another important resource for opinion mining and sentiment analysis is WordNet-Affect, which in turn lacks quantitative information. We report a work on automatically merging these two resources by assigning emotion labels to more than 2700 concepts.
Keywords :
database management systems; natural language processing; semantic Web; SenticNet; WordNet-Affect emotion lists; emotion labels; natural language processing; numerical polarity scores; opinion mining; semantic resources; sentiment analysis; sentiment-related information; Sentic computing; emotions; sentiment analysis;
Conference_Titel :
Signal Processing (ICSP), 2012 IEEE 11th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-2196-9
DOI :
10.1109/ICoSP.2012.6491803
