Course Material

Code & Data

Codes and data will be posted closer to the start of the workshop.

Starter codes: click here.

Instructor Codes: click here

Tokenized data: click here

Background Reading

Here's some background reading on text as data in marketing and economics research. You do not need to have read this prior to the workshop. Think of it as further context if you are looking to dig deeper later on.

Text as Data in R

This is a go-to book for "doing" text analytics in R:

• Tidy Text Mining with R

Some Tools We Use in this Workshop

• VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text
• stm: R Package for Structural Topic Models
• Training, evaluating, and interpreting topic models

Survey Papers

These readings are designed to provide you with some background on text as data in research.

From the Marketing Literature

• Jonah Berger, Ashlee Humphreys, Stephan Ludwig, Wendy W. Moe, Oded Netzer, and David A. Schweidel. 2020. "Uniting the Tribes: Using Text for Marketing Insight". DOI: 10.1177/0022242919873106
• Hartmann, J. and Netzer, O. 2023, "Natural Language Processing in Marketing". Review of Marketing Research. DOI: 10.1108/S1548-643520230000020011

From the Economics literature

These are a little technical and less "hands on":

• Gentzkow, Matthew, Bryan Kelly, and Matt Taddy. 2019. "Text as Data." Journal of Economic Literature, 57 (3): 535-74. DOI: 10.1257/jel.20181020
• Ash E, and Stephan Hansen. Forthcoming. "Text Algorithms in Economics." Annual Review of Economics. Web link to Working Paper

Text as Data Marketing

Some recent work using text as data tools in marketing (not an exhaustive list):

• "User-Generated Physician Ratings and Their Effects on Patients' Physician Choices: Evidence from Yelp," Yiwei Chen and Stephanie Lee, Journal of Marketing, forthcoming.
• "What Holds Attention? Linguistic Drivers of Engagement," Jonah Berger, Wendy W. Moe, and David A. Schweidel. Journal of Marketing, forthcoming.
• "Capturing changes in social media content: A multiple latent changepoint topic model," N Zhong and D Schweidel. Marketing Science, 2020.
• "Charting the path to purchase using topic models," H Li and N Ma, Journal of Marketing Research, 2020.
• "The squeaky wheel gets the grease—An empirical analysis of customer voice and firm intervention on Twitter" L Ma, B Sun and S Kekre. Marketing Science, 2015.
• "Listening in on social media: A joint model of sentiment and venue format choice", Wendy W. Moe, and David A. Schweidel. Journal of Marketing Research, 2014.
• "Online Word of Mouth and the Performance of New Products," Lachlan Deer, Pradeep K. Chintagunta and Gregory S. Crawford. Working Paper.