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Content classifiers predict the likelihood that the given content belongs to one or more IAB categories.

Machine learning predicts threat categories by applying data models trained on collections of various kinds of threatening content. 

Machine learning predicts event categories by applying data models trained on large-scale collections of event-related content pages.

A set of rules derives, scores, and ranks the most important keywords from content based on prominence and term frequency–inverse document frequency (TF-IDF) scores.

Machine learning predicts the sentiment of each sentence on within content by applying models trained on content with varying tones of voice. Verity returns an aggregated breakdown of the proportion of sentences in the content that are positive, neutral or negative (referred to as Document Level Sentiment Analysis).