Generalized Word Shift Graphs：一种文本两两比较的可视化和解释方法
Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts
摘要：
计算文本分析中的一个常见任务是根据词频，情感或信息内容等测量来量化两个语料库的差异。然而，将文本的丰富故事压缩成一个数字通常在概念上是危险的，并且很难自信地解释有趣或意想不到的文本模式，而不担心数据伪影或测量有效性。为了更好地捕捉文本之间的细粒度差异，我们引入了广义词移图，可视化产生了一个有意义的和可解释的总结，单个单词如何有助于两个文本之间的变化，任何措施，可以制定为加权平均值。我们发现，这个框架自然包括许多最常用的方法比较文本，包括相对频率，字典分数，和基于熵的措施，如Kullback-Leibler和詹森-香农分歧。通过几个案例研究，我们展示了如何广义词移图可以灵活地应用于诊断调查，假设生成和实质性的解释跨域。通过提供语料库之间文本转换的详细镜头，广义词移图帮助计算社会科学家，数字人文主义者和其他文本分析从业者形成更强大的科学叙述。
英文摘要：
A common task in computational text analyses is to quantify how two corpora differ according to a measurement like word frequency, sentiment, or information content. However, collapsing the texts' rich stories into a single number is often conceptually perilous, and it is difficult to confidently interpret interesting or unexpected textual patterns without looming concerns about data artifacts or measurement validity. To better capture fine-grained differences between texts, we introduce generalized word shift graphs, visualizations which yield a meaningful and interpretable summary of how individual words contribute to the variation between two texts for any measure that can be formulated as a weighted average. We show that this framework naturally encompasses many of the most commonly used approaches for comparing texts, including relative frequencies, dictionary scores, and entropy-based measures like the Kullback-Leibler and Jensen-Shannon divergences. Through several case studies, we demonstrate how generalized word shift graphs can be flexibly applied across domains for diagnostic investigation, hypothesis generation, and substantive interpretation. By providing a detailed lens into textual shifts between corpora, generalized word shift graphs help computational social scientists, digital humanists, and other text analysis practitioners fashion more robust scientific narratives.
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