Process / pipeline
文本回归——从文本预测数字
文本回归（Text Regression）利用从文本中提取的特征（如TF-IDF分数、词嵌入或n-gram）作为自变量，来预测一个连续的目标变量。该方法建立在Gentzkow、Kelly和Taddy（2019）整合的“文本即数据”项目之上，可以直接从文档中估计价格、评分或情感分数等数值结果，并广泛应用于社会科学、经济学和金融领域。
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来源
- Gentzkow, M., Kelly, B. & Taddy, M. (2019). Text as Data. Journal of Economic Literature, 57(3), 535-574. DOI: 10.1257/jel.20181020 ↗
- Taddy, M. (2013). Measuring Political Sentiment on Twitter: Factor Optimal Design for Multinomial Inverse Regression. Technometrics, 55(4), 415-425. DOI: 10.1080/00401706.2013.778791 ↗
如何引用本页
ScholarGate. (2026, June 1). Text-Based Regression. ScholarGate. https://scholargate.app/zh/text-mining/text-regression
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