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Consumers increasingly share their opinions about products in social media. However, the analysis of this user-generated content is limited either to small, in-depth qualitative analyses or to larger but often more superficial analyses based on word frequencies. Using the example of online comments about organic food, we investigate the relationship between qualitative analyses and latest deep neural networks in three steps. First, a qualitative content analysis defines a class system of opinions. Second, a pre-trained neural network, the Universal Sentence Encoder, analyzes semantic features for each class. Third, we show by manual inspection and descriptive statistics that these features match with the given class structure from our qualitative study. We conclude that semantic features from deep pre-trained neural networks have the potential to serve for the analysis of larger data sets, in our case on organic food. We exemplify a way to scale up sample size while maintaining the detail of class systems provided by qualitative content analyses. As the USE is pretrained on many domains, it can be applied to different domains than organic food and support consumer and public opinion researchers as well as marketing practitioners in further uncovering the potential of insights from user-generated content.

Combining content analysis and neural networks to analyze discussion topics in online comments about organic food