In this paper, we examine how specific features of language drive consumer behavior. Our contribution, however, lies not in testing specific hypotheses; rather, it is in demonstrating a data-driven process for generating them. We devise an approach that generates interpretable hypotheses from text by integrating large-language models (LLMs), machine learning (ML), and psychology experiments. Using a dataset with over 60,000 headlines (and over 32,000 A/B tests), we produce human-interpretable hypotheses about what features of language might affect engagement. We then test a subset of these hypotheses out-of-sample using two datasets: one consisting of 1,600 A/B tests and another containing over 5,000 social media posts. Our approach indeed facilitates discovery. For instance, we find that describing physical reactions significantly increases engagement. In contrast, focusing on positive aspects of human behavior decreases it. A third hypothesis posited that referring to multimedia (e.g., GIFs, videos) would influence engagement, and it does, only it significantly increases engagement in one domain while significantly decreasing it in another. This approach extends beyond a single application. In general, it offers a data-driven method for discovery that can convert unstructured text data into insights that are interpretable, novel, testable, and generalizable. It does so while maintaining a transparent role for both human researchers and algorithmic processes. This approach offers a practical tool to researchers, organizations, and policymakers seeking to aggregate insights from multiple marketing experiments.