Large Scale Personality Classification of Bloggers

Francisco Iacobelli, Alastair Gill, Scott Nowson, Jon Oberlander

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Personality is a fundamental component of an individual’s affective behavior. Previous work on personality classification has emerged from disparate sources: Varieties of algorithms and feature-selection across spoken and written data have made comparison difficult. Here, we use a large corpus of blogs to compare classification feature selection; we also use these results to identify characteristic language information relating to personality. Using Support Vector Machines, the best accuracies range from 84.36% (openness to experience) to 70.51% (neuroticism). To achieve these results, the best performing features were a combination of: (1) stemmed bigrams; (2) no exclusion of stopwords (i.e. common words); and (3) the boolean, presence or absence of features noted, rather than their rate of use. We take these findings to suggest that both the structure of the text and the presence of common words are important. We also note that a common dictionary of words used for content analysis (LIWC) performs less well in this classification task, which we propose is due to their conceptual breadth. To get a better sense of how personality is expressed in the blogs, we explore the best performing features and discuss how these can provide a deeper understanding of personality language behavior online.
Original languageEnglish
Title of host publicationAffective Computing and Intelligent Interaction
Subtitle of host publicationFourth International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part II
EditorsSidney D'Mello, Arthur Graesser, Björn Schuller, Jean-Claude Martin
PublisherSpringer-Verlag GmbH
Number of pages10
ISBN (Electronic)978-3-642-24571-8
ISBN (Print)978-3-642-24570-1
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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