An Attention-Based Denoising Framework for Personality Detection in
  Social Media Texts
Qirui Tang Wenkang Jiang Yihua Du Lei Lin
In social media networks, users produce a large amount of text content
anytime, providing researchers with a valuable approach to digging for
personality-related information. Personality detection based on user-generated
texts is a universal method that can be used to build user portraits. The
presence of noise in social media texts hinders personality detection. However,
previous studies have not fully addressed this challenge. Inspired by the
scanning reading technique, we propose an attention-based information
extraction mechanism (AIEM) for long texts, which is applied to quickly locate
valuable pieces of information, and focus more attention on the deep semantics
of key pieces. Then, we provide a novel attention-based denoising framework
(ADF) for personality detection tasks and achieve state-of-the-art performance
on two commonly used datasets. Notably, we obtain an average accuracy
improvement of 10.2% on the gold standard Twitter-Myers-Briggs Type Indicator
(Twitter-MBTI) dataset. We made our code publicly available on GitHub. We shed
light on how AIEM works to magnify personality-related signals.