机器学习方法未能从语义嵌入中提供人格特质的理论性连贯构建
Machine learning methods fail to provide cohesive atheoretical construction of personality traits from semantic embeddings
AI总结：
研究通过分析一百万条Reddit评论，比较了基于机器学习的自下而上人格模型与Big Five模型，发现机器学习聚类缺乏心理测量连贯性，未能超越Big Five的描述效用，表明机器学习无法替代既定心理学理论。
摘要：
词汇假说认为人格特质编码在语言中，并且是Big Five等模型的基础。我们使用机器学习从经典形容词列表创建了一个自下而上的人格模型，并通过分析一百万条Reddit评论，将其描述效用与Big Five进行了比较。Big Five，特别是Agreeableness、Conscientiousness和Neuroticism，对这些在线社区提供了更强大且更易解释的描述。相比之下，我们的机器学习聚类没有提供有意义的区分，未能恢复Extraversion特质，并且缺乏Big Five的心理测量连贯性。这些结果证实了Big Five的稳健性，并表明人格的语义结构是依赖于上下文的。我们的研究结果表明，尽管机器学习可以帮助检验既定心理学理论的生态效度，但它可能无法替代它们。
英文摘要：
The lexical hypothesis posits that personality traits are encoded in language and is foundational to models like the Big Five. We created a bottom-up personality model from a classic adjective list using machine learning and compared its descriptive utility against the Big Five by analyzing one million Reddit comments. The Big Five, particularly Agreeableness, Conscientiousness, and Neuroticism, provided a far more powerful and interpretable description of these online communities. In contrast, our machine-learning clusters provided no meaningful distinctions, failed to recover the Extraversion trait, and lacked the psychometric coherence of the Big Five. These results affirm the robustness of the Big Five and suggest personality's semantic structure is context-dependent. Our findings show that while machine learning can help check the ecological validity of established psychological theories, it may not be able to replace them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
