ABSTRACT Advances in the Natural Language Processing (NLP) and machine learning fields have led to the development of automated methods for the recognition of personality traits from text available from social media and similar sources. Systems of this kind exploit the close relation between lexical knowledge and personality models – such as the well-known Big Five model – to provide information about the author of an input text in a non-intrusive fashion, and at a low cost. Although now a well-established research topic in the field, the computational recognition of personality traits from text still leaves a number of research questions worth further exploration. In particular, this paper attempts to shed light on three main issues: (i) whether we may develop psycholinguistics-motivated models of personality recognition when such knowledge sources are not available for the target language under consideration; (ii) whether the use of psycholinguistic knowledge may be still superior to contemporary word vector representations; and (iii) whether we may infer certain personality facets from a corpus that does not explicitly convey this information. In this paper these issues are dealt with in a series of individual experiments of personality recognition from Facebook text, whose initial results should aid the future development of more robust systems of this kind.

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来自 Facebook 文本的计算人格识别：心理语言学特征、词语和方面

摘要 自然语言处理 (NLP) 和机器学习领域的进步导致了自动化方法的发展，用于从社交媒体和类似来源的文本中识别个性特征。此类系统利用词汇知识和个性模型（例如著名的大五模型）之间的密切关系，以非侵入性的方式以低成本提供有关输入文本作者的信息。虽然现在是该领域一个成熟的研究课题，但文本中人格特征的计算识别仍然留下了许多值得进一步探索的研究问题。特别是，本文试图阐明三个主要问题：(i) 当此类知识源不适用于所考虑的目标语言时，我们是否可以开发基于心理语言学的个性识别模型；(ii) 心理语言学知识的使用是否仍然优于当代词向量表示；(iii) 我们是否可以从没有明确传达此信息的语料库中推断出某些个性方面。在本文中，这些问题在一系列从 Facebook 文本中进行个性识别的个人实验中得到解决，其初步结果应该有助于未来开发更强大的此类系统。(iii) 我们是否可以从没有明确传达此信息的语料库中推断出某些个性方面。在本文中，这些问题在一系列从 Facebook 文本中进行个性识别的个人实验中得到解决，其初步结果应该有助于未来开发更强大的此类系统。(iii) 我们是否可以从没有明确传达此信息的语料库中推断出某些个性方面。在本文中，这些问题在一系列从 Facebook 文本中进行个性识别的个人实验中得到解决，其初步结果应该有助于未来开发更强大的此类系统。