In recent years, some researchers have turned to the use of “wordless” or thought bubbles to illustrate the most frequently utilized phrases from open ended responses in quantitative research. Often utilizing semantics scrapers and other technology to create a hierarchy of these answers, the results are a simple means to cut through extensive respondent comments. Unfortunately, such practice often eschews numerous contextual interpretations, often rendering misinformation or buried insights.
Machine learning, AI and web scrapers that identify language patterns are not a substitute for the human element of considering the appropriate context. They fail to probe respondents or apply research techniques culled from social psychology, as good qualitative researchers can. Pulling out the raw emotion and motivations behind one’s comments are even more critical in high involvement, passion driven categories like sports and travel.
In two recent projects, one for a major youth sports initiative, the other for a leading destination property, we deployed, what we called “emotional coding” to open ended responses.
Here, the SLRG research team examined comments or posts to tally frequently expressed themes, rather than just the most popular phrases that are a limitation of typical sentiment scraping software. Researchers examined what was said, how it was said, as well as what wasn’t said. We then built codes, or themes around these emotions, and examined how they correlated with other sentiments and respondent demographics and behaviors. Such an approach rendered both a quantitative read of respondent sentiment as well as a more robust sense of context that can fuel story telling and a more holistic profile of customer or guest motivations that spawned insights on how to deliver better messaging that captures the essence of these personal connections.
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