Neural user factor adaptation for text classification: Learning to generalize across author demographics
Abstract
Language usage varies across different demographic factors, such as gender, age, and geographic location. However, most existing document classification methods ignore demographic variability. In this study, we examine empirically how text data can vary across four demographic factors: gender, age, country, and region. We propose a multitask neural model to account for demographic variations via adversarial training. In experiments on four English-language social media datasets, we find that classification performance improves when adapting for user factors.
Publication Title
*SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics
Recommended Citation
Huang, X., & Paul, M. (2019). Neural user factor adaptation for text classification: Learning to generalize across author demographics. *SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics, 136-146. Retrieved from https://digitalcommons.memphis.edu/facpubs/3011
