TY - GEN
T1 - Common sense knowledge based personality recognition from text
AU - Poria, Soujanya
AU - Gelbukh, Alexandar
AU - Agarwal, Basant
AU - Cambria, Erik
AU - Howard, Newton
PY - 2013
Y1 - 2013
N2 - Past works on personality detection has shown that psycho-linguistic features, frequency based analysis at lexical level, emotive words and other lexical clues such as number of first person or second person words carry major role to identify personality associated with the text. In this work, we propose a new architecture for the same task using common sense knowledge with associated sentiment polarity and affective labels. To extract the common sense knowledge with sentiment polarity scores and affective labels we used Senticnet which is one of the most useful resources for opinion mining and sentiment analysis. In particular, we combined common sense knowledge based features with phycho-linguistic features and frequency based features and later the features were employed in supervised classifiers. We designed five SMO based supervised classifiers for five personality traits. We observe that the use of common sense knowledge with affective and sentiment information enhances the accuracy of the existing frameworks which use only psycho-linguistic features and frequency based analysis at lexical level.
AB - Past works on personality detection has shown that psycho-linguistic features, frequency based analysis at lexical level, emotive words and other lexical clues such as number of first person or second person words carry major role to identify personality associated with the text. In this work, we propose a new architecture for the same task using common sense knowledge with associated sentiment polarity and affective labels. To extract the common sense knowledge with sentiment polarity scores and affective labels we used Senticnet which is one of the most useful resources for opinion mining and sentiment analysis. In particular, we combined common sense knowledge based features with phycho-linguistic features and frequency based features and later the features were employed in supervised classifiers. We designed five SMO based supervised classifiers for five personality traits. We observe that the use of common sense knowledge with affective and sentiment information enhances the accuracy of the existing frameworks which use only psycho-linguistic features and frequency based analysis at lexical level.
KW - Affective and sentiment information
KW - Common sense knowledge
KW - Personality detection
UR - http://www.scopus.com/inward/record.url?scp=84893799597&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-45111-9_42
DO - 10.1007/978-3-642-45111-9_42
M3 - Contribución a la conferencia
SN - 9783642451102
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 484
EP - 496
BT - Advances in Soft Computing and Its Applications - 12th Mexican International Conference on Artificial Intelligence, MICAI 2013, Proceedings
T2 - 12th Mexican International Conference on Artificial Intelligence, MICAI 2013
Y2 - 24 November 2013 through 30 November 2013
ER -