Influencer marketing videos have surged in popularity, yet significant gaps remain in understanding the relationships between video features and engagement. This challenge is intensified by the complexities of interpreting unstructured data. While deep learning models effectively leverage raw unstructured data to predict engagement, they often function as black boxes with limited interpretability, particularly when human validation is hindered by the absence of a known ground truth. To address this issue, we develop an ‘interpretable deep learning framework’ that provides insights into the relationships captured by the models. Inspired by visual attention in print advertising, our interpretation approach uses measures of model attention to video features, eliminating spurious associations through a two-step process and identifying a subset of relationships for formal causal testing. This approach is versatile, as it applies across well-known attention mechanisms—additive attention, scaled dot-product attention, and gradient-based attention—when analyzing text, audio, or video image data. We apply our framework to YouTube influencer videos, linking video features to measures of shallow and deep engagement developed based on the dual-system framework of thinking. Our findings guide influencers in prioritizing the design of video features associated with deep engagement sentiment.