Using nuances of emotion to identify personality

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Proceedings titleProceedings of the ICWSM Workshop on Computational Personality Recognition
ConferenceICWSM Workshop on Computational Personality Recognition, July 2013, Boston, MA
AbstractPast work on personality detection has shown that frequency of lexical categories such as first person pronouns, past tense verbs, and sentiment words have significant correlations with personality traits. In this paper, for the first time, we show that fine affect (emotion) categories such as that of excitement, guilt, yearning, and admiration are significant indicators of personality. Additionally, we perform experiments to show that the gains provided by the fine affect categories are not obtained by using coarse affect categories alone or with specificity features alone. We employ these features in five SVM classifiers for detecting five personality traits through essays. We find that the use of fine emotion features leads to statistically significant improvement over a competitive baseline, whereas the use of coarse affect and specificity features does not.
Publication date
PublisherAAAI Publications
AffiliationNational Research Council Canada; Information and Communication Technologies
Peer reviewedYes
NPARC number21270519
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Record identifier0d2da4fd-27fd-42f1-a867-d3de866ad8ea
Record created2014-02-14
Record modified2016-05-09
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