@inproceedings{lynn-etal-2020-hierarchical,
title = "Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention",
author = "Lynn, Veronica and
Balasubramanian, Niranjan and
Schwartz, H. Andrew",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.472/",
doi = "10.18653/v1/2020.acl-main.472",
pages = "5306--5316",
abstract = "Not all documents are equally important. Language processing is increasingly finding use as a supplement for questionnaires to assess psychological attributes of consenting individuals, but most approaches neglect to consider whether all documents of an individual are equally informative. In this paper, we present a novel model that uses message-level attention to learn the relative weight of users' social media posts for assessing their five factor personality traits. We demonstrate that models with message-level attention outperform those with word-level attention, and ultimately yield state-of-the-art accuracies for all five traits by using both word and message attention in combination with past approaches (an average increase in Pearson r of 2.5{\%}). In addition, examination of the high-signal posts identified by our model provides insight into the relationship between language and personality, helping to inform future work."
}
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<abstract>Not all documents are equally important. Language processing is increasingly finding use as a supplement for questionnaires to assess psychological attributes of consenting individuals, but most approaches neglect to consider whether all documents of an individual are equally informative. In this paper, we present a novel model that uses message-level attention to learn the relative weight of users’ social media posts for assessing their five factor personality traits. We demonstrate that models with message-level attention outperform those with word-level attention, and ultimately yield state-of-the-art accuracies for all five traits by using both word and message attention in combination with past approaches (an average increase in Pearson r of 2.5%). In addition, examination of the high-signal posts identified by our model provides insight into the relationship between language and personality, helping to inform future work.</abstract>
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%0 Conference Proceedings
%T Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention
%A Lynn, Veronica
%A Balasubramanian, Niranjan
%A Schwartz, H. Andrew
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F lynn-etal-2020-hierarchical
%X Not all documents are equally important. Language processing is increasingly finding use as a supplement for questionnaires to assess psychological attributes of consenting individuals, but most approaches neglect to consider whether all documents of an individual are equally informative. In this paper, we present a novel model that uses message-level attention to learn the relative weight of users’ social media posts for assessing their five factor personality traits. We demonstrate that models with message-level attention outperform those with word-level attention, and ultimately yield state-of-the-art accuracies for all five traits by using both word and message attention in combination with past approaches (an average increase in Pearson r of 2.5%). In addition, examination of the high-signal posts identified by our model provides insight into the relationship between language and personality, helping to inform future work.
%R 10.18653/v1/2020.acl-main.472
%U https://aclanthology.org/2020.acl-main.472/
%U https://doi.org/10.18653/v1/2020.acl-main.472
%P 5306-5316
Markdown (Informal)
[Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention](https://aclanthology.org/2020.acl-main.472/) (Lynn et al., ACL 2020)
ACL