Incorporating LIWC in Neural Networks to Improve Human Trait and Behavior Analysis in Low Resource Scenarios

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2022.lrec-1.482.pdf (309.32 KB)

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http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.482.pdf

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http://hdl.handle.net/11603/25381

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https://orcid.org/0000-0002-5989-8543

Date

2022-06

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conference papers and proceedings
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Kılıc, Isıl Doga Yakut and Shimei Pan. "Incorporating LIWC in Neural Networks to Improve Human Trait and Behavior Analysis in Low Resource Scenarios." Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), Marseille (June 2022). http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.482.pdf

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Abstract

Psycholinguistic knowledge resources have been widely used in constructing features for text-based human trait and behavior analysis. Recently, deep neural network (NN)-based text analysis methods have gained dominance due to their high prediction performance. However, NN-based methods may not perform well in low resource scenarios where the ground truth data is limited (e.g., only a few hundred labeled training instances are available). In this research, we investigate diverse methods to incorporate Linguistic Inquiry and Word Count (LIWC), a widely-used psycholinguistic lexicon, in NN models to improve human trait and behavior analysis in low resource scenarios. We evaluate the proposed methods in two tasks: predicting delay discounting and predicting drug use based on social media posts. The results demonstrate that our methods perform significantly better than baselines that use only LIWC or only NN-based feature learning methods. They also performed significantly better than published results on the same dataset.