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Personality Facets Recognition from Text

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Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11696))

Abstract

Fundamental Big Five personality traits (e.g., Extraversion) and their facets (e.g., Activity) are known to correlate with a broad range of linguistic features and, accordingly, the recognition of personality traits from text is a well-known Natural Language Processing task. Labelling text data with facets information, however, may require the use of lengthy personality inventories, and perhaps for that reason existing computational models of this kind are usually limited to the recognition of the fundamental traits. Based on these observations, this paper investigates the issue of personality facets recognition from text labelled only with information available from a shorter personality inventory. In doing so, we provide a low-cost model for the recognition of certain personality facets, and present reference results for further studies in this field.

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Acknowledgements

This work received support by FAPESP grant 2017/06828-1 and 2016/14223-0.

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Correspondence to Ivandré Paraboni .

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dos Santos, W.R., Paraboni, I. (2019). Personality Facets Recognition from Text. In: Crestani, F., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2019. Lecture Notes in Computer Science(), vol 11696. Springer, Cham. https://doi.org/10.1007/978-3-030-28577-7_15

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  • DOI: https://doi.org/10.1007/978-3-030-28577-7_15

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  • Online ISBN: 978-3-030-28577-7

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