Abstract: The technology is generally directed to identifying and providing relevant digital content associated with digital components. The digital content may be identified based on a comparison of identifiers associated with the digital component to identifiers associated with the digital content. The digital component may include a content identifier, which may be used to determine one or more merchant identifiers. The merchant identifiers may be associated with brands, product types, or the like within the digital component. The merchant identifiers may be compared to content index identifiers. The content index identifiers may be used to identify the brands, product types, merchants, etc., of the digital content in the content index. When the merchant identifier matches a content index identifier, the digital content associated with the matching content index identifier may be identified as relevant digital content. The relevant digital content may be provided for output relative to the digital component.

Abstract: The technology is generally directed to personalizing digital components for users based on a merged preference profile. The merged preference profile may be generated based on declared and inferred preferences of the users. The declared and inferred preferences may be preferences related to brands, topics, types of products, etc. The merged preference profile may be used to personalize the digital components output to the user. For example, when identifying digital components to be selected, at least some of the digital components may be selected based on the merged preference profile of the user. At least a portion of the selected digital components may be digital components with a subject matching the preferences of the user, thereby improving the digital components output to the user.

Abstract: Systems and methods for selecting visual elements to insert into content items based on sentiment analysis are detailed herein. A data processing system can establish a performance prediction model for content items correlating constituent visual elements to sentiment performance metrics using a training dataset. The data processing system can identify a content item and candidate visual elements to insert. The content item can have constituent visual elements. The data processing system can determine a total sentiment performance metric for the content item using the performance prediction model. The data processing system can determine a combinative performance metric between the candidate visual element and the visual elements using the performance prediction model. The combinative performance metric can indicate a predicted effect on the total performance metric. The data processing system can select a candidate visual element to insert into the content item based on the combinative performance metric.