By Yair Neuman
The emergence of clever applied sciences, subtle normal language processing methodologies and enormous textual repositories, invitations a brand new technique for the problem of instantly determining character dimensions in the course of the research of textual information. This brief booklet goals to (1) introduce the problem of computational character research, (2) current a different method of character research and (3) illustrate this method via case reports and worked-out examples.
This booklet is of detailed relevance to psychologists, in particular these drawn to the hot insights provided by means of new computational and data-intensive instruments, and to computational social scientists attracted to human character and language processing.
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Extra info for Computational Personality Analysis: Introduction, Practical Applications and Novel Directions
Botvinick, M. (2013). Divide and conquer: Hierarchical reinforcement learning and task decomposition in humans. In G. Baldassarre & M. ), Computational and robotic models of the hierarchical organization of behavior (pp. 271–291). Berlin, Germany: Springer. , & Owen, D. (2014). Linguistic biomarkers of hubris syndrome. Cortex, 55, 167–181. , Bengio, Y. & Curville, A. (2016). Deep learning. deeplearningbook. org. Accessed April 20, 2016. Guyon, I. & Elisseeff, A. (2003). An introduction to variable and feature selection.
It may be interesting to use measures that express the complexity of human personality (by representing its dynamic aspect, for instance) and to use those measures as features of ML algorithms Several such ideas will be explored in the following chapters. ” References 37 References Bandt, C. & Pompe, B. (2002). Permutation entropy: A natural complexity measure for time series. Physical Review Letters, 88(17), 174102. Botvinick, M. M. (2012). Hierarchical reinforcement learning and decision making.
Or more sophisticated measures that capture the macro-level structure of the whole text. For example, we may transform the text into a network of words and study it through various network measures, as will be illustrated later. It goes without saying that features at one level of analysis can be used to build features at higher levels of analysis: words can be grouped into categories and propositions can be used to build arguments, such as in the context of trying to identify political attitudes.