Personality Trait Identification Using the Russian Feature Extraction Toolkit

James R. Hull, Valerie Novak, C. Anton Rytting, Paul Rodrigues, Victor M. Frank, Matthew Swahn


Abstract
Feature engineering is an important step in classical NLP pipelines, but machine learning engineers may not be aware of the signals to look for when processing foreign language text. The Russian Feature Extraction Toolkit (RFET) is a collection of feature extraction libraries bundled for ease of use by engineers who do not speak Russian. RFET’s current feature set includes features applicable to social media genres of text and to computational social science tasks. We demonstrate the effectiveness of the tool by using it in a personality trait identification task. We compare the performance of Support Vector Machines (SVMs) trained with and without the features provided by RFET; we also compare it to a SVM with neural embedding features generated by Sentence-BERT.
Anthology ID:
2021.ranlp-1.66
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
583–592
Language:
URL:
https://aclanthology.org/2021.ranlp-1.66
DOI:
Bibkey:
Cite (ACL):
James R. Hull, Valerie Novak, C. Anton Rytting, Paul Rodrigues, Victor M. Frank, and Matthew Swahn. 2021. Personality Trait Identification Using the Russian Feature Extraction Toolkit. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 583–592, Held Online. INCOMA Ltd..
Cite (Informal):
Personality Trait Identification Using the Russian Feature Extraction Toolkit (Hull et al., RANLP 2021)
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PDF:
https://aclanthology.org/2021.ranlp-1.66.pdf