Paper Info
Title
EmoSenticSpace: A novel framework for affective common-sense reasoning.
Abstract
Emotions play a key role in natural language understanding and sensemaking. Pure machine learning usually fails to recognize and interpret emotions in text accurately. The need for knowledge bases that give access to semantics and sentics (the conceptual and affective information) associated with natural language is growing exponentially in the context of big social data analysis. To this end, this paper proposes EmoSenticSpace, a new framework for affective common-sense reasoning that extends WordNet-Affect and SenticNet by providing both emotion labels and polarity scores for a large set of natural language concepts. The framework is built by means of fuzzy c-means clustering and support-vector-machine classification, and takes into account a number of similarity measures, including point-wise mutual information and emotional affinity. EmoSenticSpace was tested on three emotion-related natural language processing tasks, namely sentiment analysis, emotion recognition, and personality detection. In all cases, the proposed framework outperforms the state-of-the-art. In particular, the direct evaluation of EmoSenticSpace against psychological features provided in the benchmark ISEAR dataset shows a 92.15% agreement.
Year
DOI
Venue
2014
10.1016/j.knosys.2014.06.011
Knowledge-Based Systems
Keywords
Field
DocType
Sentic computing,Opinion mining,Sentiment analysis,Emotion recognition,Personality detection,Fuzzy clustering
Data mining,Sentiment analysis,Computer science,Commonsense reasoning,Sensemaking,Natural language understanding,Natural language,Social data analysis,Artificial intelligence,Cluster analysis,Machine learning,Semantics
Journal
Volume
Issue
ISSN
69
1
0950-7051
Citations 
PageRank 
References 
50
1.55
39
Authors
5
Name
Order
Citations
PageRank
Soujanya Poria1133660.98
Alexander Gelbukh22843269.19
Erik Cambria33873183.70
Amir Hussain470529.16
Guang-Bin Huang511303470.52