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This article uses a multitheoretical approach to investigate the relationship between language use and opinion expression on Yelp. Using review metadata (e.g., star rating) to observe variation in reviewer feelings and motivations, we test for the strength of different message design logics: expressive logics, where language reflects a reviewer’s underlying opinion, and rhetorical logics, where language reflects a reviewer’s desire to make his or her opinion credible and acceptable to their audience. Results suggest that emotional language is motivated by expression as higher rated businesses are reviewed with more positive and fewer negative emotion terms. Rhetorical logics are associated with the use of abstract and self-focused language, with analysis suggesting this may result from the reviewer’s decision to write either narratively or formally.

A Multitheoretical Approach to Big Text Data: Comparing Expressive and Rhetorical Logics in Yelp Reviews