期刊名称：Journal of Automation, Mobile Robotics & Intelligent Systems (JAMRIS)
印刷版ISSN：1897-8649
电子版ISSN：2080-2145
出版年度：2007
卷号：30
页码：457-500
出版社：Industrial Research Inst. for Automation and Measurements, Warsaw
摘要：It is well known that utterances convey a great deal of information about the
speaker in addition to their semantic content. One such type of information
consists of cues to the speaker's personality traits, the most fundamental
dimension of variation between humans. Recent work explores the automatic
detection of other types of pragmatic variation in text and conversation, such
as emotion, deception, speaker charisma, dominance, point of view, subjectivity,
opinion and sentiment. Personality affects these other aspects of linguistic
production, and thus personality recognition may be useful for these tasks, in
addition to many other potential applications. However, to date, there is little
work on the automatic recognition of personality traits. This article reports
experimental results for recognition of all Big Five personality traits, in both
conversation and text, utilising both self and observer ratings of personality.
While other work reports classification results, we experiment with
classification, regression and ranking models. For each model, we analyse the
effect of different feature sets on accuracy. Results show that for some traits,
any type of statistical model performs significantly better than the baseline,
but ranking models perform best overall. We also present an experiment
suggesting that ranking models are more accurate than multi-class classifiers
for modelling personality. In addition, recognition models trained on observed
personality perform better than models trained using self-reports, and the
optimal feature set depends on the personality trait. A qualitative analysis of
the learned models confirms previous findings linking language and personality,
while revealing many new linguistic markers.
