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We combined established psychological measures with techniques in machine learning to measure changes in gender stereotypes over the course of the 20th century as expressed in large-scale historical natural language data. Although our analysis replicated robust gender biases previously documented in the literature, we found that the strength of these biases has diminished over time. This appears to be driven by changes in gender biases for stereotypically feminine traits (rather than stereotypically masculine traits) and changes in gender biases for personality-related traits (rather than physical traits). Our results illustrate the dynamic nature of stereotypes and show how recent advances in data science can be used to provide a long-term historical analysis of core psychological variables. In terms of practice, these findings may, albeit cautiously, suggest that women and men can be less constrained by prescriptions of feminine traits. Additional online materials for this article are available on PWQ’s website at 10.1177/0361684320977178