从文档级用户响应中学习移情和痛苦的单词评级
Learning Word Ratings for Empathy and Distress from Document-Level User Responses
摘要：
尽管黑盒方法在建模情感和情绪方面表现出色，但表征不同情绪的词汇（信息词和相关权重的集合）对NLP社区来说是不可或缺的，因为它们允许可解释和鲁棒的预测。文本的情感分析在NLP中越来越受欢迎;然而，手动创建心理结构（如移情）的词汇已被证明是困难的。本文自动创建移情词评级从文档级评级。迄今为止，从更高级别的监督中学习单词评级的潜在问题仅以临时的方式得到解决，并且尚未使用深度学习方法。我们系统地比较了许多从更高级别的监督中学习单词评级的方法，以及我们发现表现最好的混合级前馈网络（MLFFN），并使用MLFFN创建了有史以来第一个同理心词典。然后，我们使用符号谱聚类来深入了解结果单词。同理心和痛苦词典可在http://www.wwbp.org/lexica.html上公开获取。
英文摘要：
Despite the excellent performance of black box approaches to modeling sentiment and emotion, lexica (sets of informative words and associated weights) that characterize different emotions are indispensable to the NLP community because they allow for interpretable and robust predictions. Emotion analysis of text is increasing in popularity in NLP; however, manually creating lexica for psychological constructs such as empathy has proven difficult. This paper automatically creates empathy word ratings from document-level ratings. The underlying problem of learning word ratings from higher-level supervision has to date only been addressed in an ad hoc fashion and has not used deep learning methods. We systematically compare a number of approaches to learning word ratings from higher-level supervision against a Mixed-Level Feed Forward Network (MLFFN), which we find performs best, and use the MLFFN to create the first-ever empathy lexicon. We then use Signed Spectral Clustering to gain insights into the resulting words. The empathy and distress lexica are publicly available at: http://www.wwbp.org/lexica.html.
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。
