文本分析在经管领域中的应用概述

早先的营销领域，如在线评论文本分析指标多为

• 数量，如文本长度(e.g., Godes and Mayzlin 2004; Moe and Trusov2011)

• 情感得分(效价，评论评分) (e.g., Godes and Silva 2012; Moe and Schweidel 2012; Ying, Feinberg and Wedel 2006)·

• 方差，如信息墒(e.g., Godes and Mayzlin 2004).

然而如今这些指标经常忽略了文本的丰富度。以下几种是更好用的指标

据被压缩成词组频数，定性的文本数据转化为定量的频数。本课程中会涉及到的内容(x到的部分)

• [ ] Thematic Analysis 定性

• [x] Content Analysis

• [x] Dictionary

• [x] Bag of words 词袋法

• [x] Supervised ，监督学习 文本分类问题

• [x] Unsupervised，如非监督LDA话题模型

• [ ] Natural language processing

相关文献

[1]Berger, Jonah, Ashlee Humphreys, Stephan Ludwig, Wendy W. Moe, Oded Netzer, and David A. Schweidel. "Uniting the tribes: Using text for marketing insight." Journal of Marketing (2019): 0022242919873106.

[2]Kenneth Benoit. July 16, 2019. “[Text as Data: An Overview](https://kenbenoit.net/pdfs/28 Benoit Text as Data draft 2.pdf).” Forthcoming in Cuirini, Luigi and Robert Franzese, eds. Handbook of Research Methods in Political Science and International Relations . Thousand Oaks: Sage.

[3]Banks, George C., Haley M. Woznyj, Ryan S. Wesslen, and Roxanne L. Ross. "A review of best practice recommendations for text analysis in R (and a user-friendly app)." Journal of Business and Psychology 33, no. 4 (2018): 445-459.