面向社交媒体文本人格检测的基于注意力的去噪框架
An Attention-Based Denoising Framework for Personality Detection in Social Media Texts
AI总结：
针对社交媒体文本噪声阻碍人格检测的问题，提出基于注意力的去噪框架ADF和信息提取机制AIEM，在Twitter-MBTI等数据集上取得最优性能，准确率平均提升10.2%。
摘要：
在社交媒体网络中，用户随时产生大量文本内容，为研究人员提供了挖掘人格相关信息的宝贵途径。基于用户生成文本的人格检测方法具有广阔的应用前景，例如用于构建用户画像。社交媒体文本中存在的显著噪声阻碍了人格检测。然而，以往的研究并未深入探讨如何应对这一挑战。受扫描阅读技术的启发，我们提出了一种针对长文本的基于注意力的信息提取机制（AIEM），用于快速定位有价值的文本片段，并充分整合有益的语义信息。然后，我们为人格检测任务提供了一种新颖的基于注意力的去噪框架（ADF），并在两个常用数据集上取得了最先进的性能。值得注意的是，我们在黄金标准 Twitter-Myers-Briggs Type Indicator（Twitter-MBTI）数据集上获得了 10.2% 的平均准确率提升。我们在 GitHub 上公开了代码。我们通过案例研究阐明了 AIEM 如何工作以放大人格相关信号。
英文摘要：
In social media networks, users produce a large amount of text content anytime, providing researchers with an invaluable approach to digging for personality-related information. Personality detection based on user-generated text is a method with broad application prospects, such as for constructing user portraits. The presence of significant noise in social media texts hinders personality detection. However, previous studies have not delved deeper into addressing 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 text, and fully integrate beneficial semantic information. 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\footnote{https://github.com/Once2gain/PersonalityDetection}. We shed light on how AIEM works to magnify personality-related signals through a case study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
