Personality Predictive Lexical Cues and Their Correlations

Xiao-li, HE; Melo, Gerard de

doi:10.26615/978-954-452-072-4_058

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Supervised learning has been the predominant approach, relying on text datasets labelled with personality traits via either self-report or crowdsourced annotations. Among many others, log-linear models (Volkova et al, 2015), random forests , GloVe embeddings with Gaussian processes (Arnoux et al, 2017), recurrent neural networks (Liu et al, 2017), convolutional neural networks (Majumder et al, 2017), support vector machines (Lan and Paraboni, 2018), ridge regression (He and de Melo, 2021), graphical networks (Yang et al, 2021b) and transformers (Kreuter et al, 2022) have been used. Another popular, psycho-linguistically motivated approach are dictionaries/lexicons (e.g., Oberlander and Nowson, 2006;Sinha et al, 2015;Das and Das, 2017).…”

Section: Current Tpc In a Nutshellmentioning

confidence: 99%

On Text-based Personality Computing: Challenges and Future Directions

Fang¹,

Giachanou²,

Bagheri³

et al. 2023

Findings of the Association for Computational Linguistics: ACL 2023

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Text-based personality computing (TPC) has gained many research interests in NLP. In this paper, we describe 15 challenges that we consider deserving the attention of the NLP research community. These challenges are organized by the following topics: personality taxonomies, measurement quality, datasets, performance evaluation, modelling choices, as well as ethics and fairness. When addressing each challenge, not only do we combine perspectives from both NLP and social sciences, but also offer concrete suggestions. We hope to inspire more valid and reliable TPC research.

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