TY - JOUR
T1 - EmoSenticSpace
T2 - A novel framework for affective common-sense reasoning
AU - Poria, Soujanya
AU - Gelbukh, Alexander
AU - Cambria, Erik
AU - Hussain, Amir
AU - Huang, Guang Bin
N1 - Publisher Copyright:
© 2014 Elsevier B.V. All rights reserved.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Emotion recognition
KW - Fuzzy clustering
KW - Opinion mining
KW - Personality detection
KW - Sentic computing
KW - Sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=84924596504&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2014.06.011
DO - 10.1016/j.knosys.2014.06.011
M3 - Artículo
SN - 0950-7051
VL - 69
SP - 108
EP - 123
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
IS - 1
ER -