Automated Text Analysis for Consumer Research

TLDR

The growing volume of digital text—from online consumer discussions, product reviews, and news archives—offers rich sources for consumer researchers to explore attitudes, interactions, and culture. The article aims to overview automated text analysis, integrating linguistic theory with consumer research constructs, and to guide method selection while addressing sampling and statistical challenges. It reviews automated text analysis techniques, linking linguistic theory to consumer constructs, and offers guidance on method choice and on handling sampling and statistical issues. The authors contend that automated text analysis, while not universally applicable, is valuable for uncovering patterns and for studying psychological and sociological constructs in consumer-generated text, offering both discovery and ecological validity.

Abstract

Abstract The amount of digital text available for analysis by consumer researchers has risen dramatically. Consumer discussions on the internet, product reviews, and digital archives of news articles and press releases are just a few potential sources for insights about consumer attitudes, interaction, and culture. Drawing from linguistic theory and methods, this article presents an overview of automated text analysis, providing integration of linguistic theory with constructs commonly used in consumer research, guidance for choosing amongst methods, and advice for resolving sampling and statistical issues unique to text analysis. We argue that although automated text analysis cannot be used to study all phenomena, it is a useful tool for examining patterns in text that neither researchers nor consumers can detect unaided. Text analysis can be used to examine psychological and sociological constructs in consumer-produced digital text by enabling discovery or by providing ecological validity.