An Attention-Based Denoising Framework for Personality Detection in Social Media Texts

• URL: http://arxiv.org/abs/2311.09945v1
• Date: Thu, 16 Nov 2023 14:56:09 GMT
• Title: An Attention-Based Denoising Framework for Personality Detection in Social Media Texts
• Authors: Qirui Tang, Wenkang Jiang, Yihua Du, Lei Lin
• Abstract summary: Personality detection based on user-generated texts is a universal method that can be used to build user portraits. We propose an attention-based information extraction mechanism (AIEM) for long texts, which is applied to quickly locate valuable pieces of information. We obtain an average accuracy improvement of 10.2% on the gold standard Twitter-Myers-Briggs Type Indicator dataset.
• Score: 1.4887196224762684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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