Traditional surveys are costly, hard to reconstruct retrospectively, and vulnerable to self-presentation bias. Raw internet text is abundant but noisy, weakly structured, and platform-selected. We introduce TaDaS (Text as Data as Survey), a framework that converts naturally occurring text into survey-like evidence by linking a question corpus to an answer corpus through cross-dataset semantic retrieval. TaDaS first screens a reference question corpus to construct focal and comparable semantic neighborhoods. It then maps unstructured observations from an answer corpus onto these neighborhoods and scores the attitudes expressed in the resulting discourse. We apply the framework to economists' reactions to AI by linking 1.3 million research-related posts from Economics Job Market Rumors with 53,585 elite economics and finance publications. Publication-side topics define the research frontier; forum-side replies reveal professional sentiment along six dimensions: openness, negativity, toxicity, arrogance, curiosity, and confusion. AI-related discussion is less open and more negative in cross-section, but the interaction evidence points in a favorable direction on all six dimensions as AI becomes more visible in elite journals. The findings show how TaDaS can recover scalable, retrospective, and non-reactive measures of professional sentiment from existing text archives.

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