Adaptive Personality Recognition from Text

We address the issue of domain adaptation for automatic Personality Recognition from Text (PRT). The PRT task consists in the classification of the personality traits of some authors, given some pieces of text they wrote. The purpose of our work is to improve current approaches to PRT in order to extract personality information from social network sites, which is a really challenging task. We argue that current approaches, based on supervised learning, have several limitations for the adaptation to social network domain, mainly due to 1) difficulties in data annotation, 2) overfitting, 3) lack of domain adaptability and 4) multilinguality issues. We propose and test a new approach to PRT, that we will call Adaptive Personality Recognition (APR). We argue that this new approach solves domain adaptability problems and it is suitable for the application in Social Network Sites. We start from an introduction that covers all the background knowledge required for understanding PRT. It includes arguments