Content analysis has enabled organizational scholars to study constructs and relationships that were previously unattainable at scale. One particular area of focus has been on sentiment analysis, which scholars have implemented to examine myriad relationships pertinent to organizational research. This article addresses certain limitations in sentiment analysis. More specifically, we bring attention to the challenge of accurately attributing sentiment in text that mentions multiple firms. Whereas traditional methods often result in measurement error due to misattributing text to firms, we offer coreference resolution—a natural language processing technique that identifies and links expressions referring to the same entity—as a solution to this problem. Across two studies, we demonstrate the potential of this approach to reduce measurement error and enhance the veracity of text analyses. We conclude by offering avenues for theoretical and empirical advances in organizational research.

中文翻译：

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使用共指分辨率减少文本分析中的测量误差

内容分析使组织学者能够研究以前无法大规模实现的结构和关系。一个特别关注的领域是情感分析，学者们已经实施了这种分析来检查与组织研究相关的无数关系。本文介绍了情绪分析中的某些限制。更具体地说，我们提请注意在提及多家公司的文本中准确归因情绪的挑战。传统方法经常由于将文本错误地归属于公司而导致测量误差，而我们提供共指解析（一种自然语言处理技术，可以识别并链接引用同一实体的表达式）作为此问题的解决方案。在两项研究中，我们展示了这种方法在减少测量误差和提高文本分析准确性方面的潜力。最后，我们为组织研究的理论和实证进步提供了途径。