This article presents a computational approach to examining immigrant incorporation through shifts in the social “mainstream.” Analyzing a historical corpus of American etiquette books, texts from 1922–2017 describing social norms, we identify mainstream shifts related to long-standing groups which once were and may currently still be seen as immigrant outsiders in the United States: Catholic, Chinese, Irish, Italian, Jewish, Mexican, and Muslim groups. The analysis takes a computational grounded theory approach, combining qualitative readings and computational text analyses. Using word embeddings, we operationalize the chosen groups as focal group concepts. We extract sections of text that are salient to the focal group concepts to create group-specific text corpora. Two computational approaches make it possible to examine mainstream shifts in these corpora. First, we use sentiment analysis to observe the positive sentiment in each corpus and its change over time. Second, we observe changes in each corpus's position on a semantic dimension represented by the poles of “strange” and “normal.” The results indicate mainstream shifts through increases in positive sentiment and movement from strange to normal over time for most of the group-specific corpora. These research techniques can be adapted to other studies of social sentiment and symbolic inclusion.