Personality is considered one of the most influential research topics in
psychology, as it predicts many consequential outcomes such as mental and
physical health and explains human behaviour. With the widespread use of social
networks as a means of communication, it is becoming increasingly important to
develop models that can automatically and accurately read the essence of
individuals based solely on their writing. In particular, the convergence of
social and computer sciences has led researchers to develop automatic
approaches for extracting and studying "hidden" information in textual data on
the internet. The nature of this thesis project is highly experimental, and the
motivation behind this work is to present detailed analyses on the topic, as
currently there are no significant investigations of this kind. The objective
is to identify an adequate semantic space that allows for defining the
personality of the object to which a certain text refers. The starting point is
a dictionary of adjectives that psychological literature defines as markers of
the five major personality traits, or Big Five. In this work, we started with
the implementation of fully-connected neural networks as a basis for
understanding how simple deep learning models can provide information on hidden
personality characteristics. Finally, we use a class of distributional
algorithms invented in 2013 by Tomas Mikolov, which consists of using a
convolutional neural network that learns the contexts of words in an
unsupervised way. In this way, we construct an embedding that contains the
semantic information on the text, obtaining a kind of "geometry of meaning" in
which concepts are translated into linear relationships. With this last
experiment, we hypothesize that an individual writing style is largely coupled
with their personality traits.

本研究使用神经网络和分布式算法来提取文本的隐藏性信息，研究人员从心理学文献定义的五大人格特征（Big Five）的形容词词典开始，并构造了包含文本语义信息的嵌入，证明了个人写作风格与其人格特征密切相关。