In this work we experiment with the hypothesis that words subjects use can be used to predict their psychological attachment style (secure, fearful, dismissing, preoccupied) as defined by Bartholomew and Horowitz. In order to verify this hypothesis, we collected a series of autobiographic texts written by a set of 202 participants. Additionally, a psychological instrument (Frías questionnaire) was applied to these same participants to measure their attachment style. We identified characteristic patterns for each style of attachment by means of two approaches: (1) mapping words into a word space model composed of unigrams, bigrams and/or trigrams on which different classifiers were trained (Naïve Bayes (NB), Bernoulli NB, Multinomial NB, Multilayer Perceptrons); and (2) using a word-embedding based representation and a neural network architecture based on different units (LSTM, Gated Recurrent Units (GRU) and Bilateral GRUs). We obtained the best accuracy of 0.4079 for the first approach by using a Boolean Multinomial NB on unigrams, bigrams and trigrams altogether, and an accuracy of 0.4031 for the second approach using Bilateral GRUs.

中文翻译：

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基于简短传记的心理依恋风格预测

在这项工作中，我们尝试使用以下假设：Bartholomew和Horowitz定义了受试者可以使用的词语来预测他们的心理依恋方式（安全，恐惧，解雇，专心）。为了验证这一假设，我们收集了由202名参与者撰写的一系列自传文本。此外，对这些参与者使用了一种心理工具（Frías调查表）来衡量他们的依恋风格。我们通过两种方法来确定每种依恋风格的特征模式：（1）将单词映射到一个单词空间模型中，该单词空间模型由训练有不同分类器的字母，二元组和/或三字组组成（朴素贝叶斯（NB），伯努利NB，多项式NB，多层感知器）; （2）使用基于词嵌入的表示和基于不同单位（LSTM，门控循环单位（GRU）和双边GRU）的神经网络架构。通过对字母组合，双字母组和三字母组合使用布尔多项式NB，对于第一种方法，我们获得了0.4079的最佳精度；对于使用双边GRU的第二种方法，我们获得了0.4031的精度。