Short-form video combines speech, imagery, and on-screen text, creating challenges for systematic content analysis. We present a five-stage multimodal framework for measuring creators' themes and communication strategies at scale. The framework integrates platform metadata, speech transcripts, and vision-language descriptions of sampled frames, then applies large language models to generate predefined indicators and open-ended labels. An audited keyword scheme consolidates thematic labels, while creator-weighted aggregation gives each account equal weight regardless of posting volume. We demonstrate the framework on 9,852 TikTok videos from sixteen creators, retaining 7,311 videos for analysis across seven themes and seven communication indicators. Automated profiles reveal substantial differences in creators' use of education, humor, and responses to current events. Validation against two independent expert annotators yielded a mean framework-expert agreement of Cohen's $κ= 0.40$ across the six binary indicators, close to the $κ= 0.42$ the two experts achieved with each other, though agreement varied substantially across individual indicators. The framework provides a modular approach to comparative analysis of creator communication, combining scalable annotation with transparent aggregation and construct-specific validation.

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