All work and no play: A text analysis

Abstract

Automated analysis of open-ended text survey data is an appealing prospect. It eliminates human error and human variability and can be used to create models that are easier to update over time than a manual approach to coding generally yields. Today, text analytics is a huge business and is among the most popular innovations within the current research landscape. However, within the research industry, there has been little change in usage in recent years, and awareness of the options available appears to be limited. We wished to look more closely at the true strengths of different approaches, the main barriers to their adoption, and how these might be overcome. Using text responses from a short survey about work and play in two markets, we contrasted two tools in analyzing the output: Q’s text analysis component and Google Cloud Natural Language. We chose these tools as they can each be easily applied to survey data but are based on different analytic principles. We found some surprising differences between the output of the two tools and between the text analysis metrics and scalar data. We conclude by discussing some of the key contemporary themes in text analytics and the likely future role of this method within market research and insight.