PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Lexical Predictors of Personality Type
Shlomo Argamon, Sushant Dhawle, Moshe Koppel and James Pennebaker
In: 2005 Joint Annual Meeting of the Interface and the Classification Society of North America, 8-12 Jun 2005, St. Louis, MO.

Abstract

We are currently pursuing methods for “author profiling” in which various aspects of the author’s identity might be identified from a text, without necessarily having a corpus of documents from the same individual. A key component of such an identity profile is personality; this paper addresses distinguishing high from low neuroticism and extraversion in authors of informal text. We consider four different sets of lexical features for this task: a standard function word list, conjunctive phrases, modality indicators, and appraisal adjectives and modifiers. SMO, a support vector machine learner, was used to learn linear separators for the high and low classes in each of the two tasks. We find that appraisal use is the best predictor for neuroticism, and that function words work best for extraversion. Further, examination of the specifically most important features yields insight into how neuroticism and extraversion differentially affect language use.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
ID Code:1492
Deposited By:Oren Glickman
Deposited On:28 November 2005