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Claims and evaluations of authenticity are a powerful resource in food discourse: reviewers use evaluations of authenticity to demonstrate their expertise, and restaurants viewed as authentic receive higher star ratings. But the multivalent nature of authenticity presents challenges for researchers. This contribution seeks to understand authenticity by combining computational and corpus driven discourse analysis methods. O’Connor et al. (2017) sought to quantify the impact of authenticity on consumer perception via four theoretical authenticity types (type, craft, moral, and idiosyncratic). This method is tested using a sample of US restaurant reviews and compared to sentiment analysis metrics computed from the same dataset. All types except for moral authenticity showed a positive effect on sentiment. Authenticity in restaurant reviews is further investigated by examining collocates of terms referring to authenticity and compiling keywords of subcorpora created from high and low scoring reviews. Reviewers most often topicalize authenticity in terms of place, taste, and descriptors of ethnicity. These findings illustrate how combining corpus driven discourse analytical and computational methods can illuminate evaluation from multiple perspectives and provide insights which may help to improve computational approaches in the future.
Dominick Boyle (Wed,) studied this question.