1. A computer-implemented method, comprising: obtaining a plurality of user-provided content items that include respective audio information and video information associated with a type of annotation item; training a set of classifiers using the plurality of user-provided content items to determine a relationship between features of the plurality of user-provided content items and the type of annotation item, the set of classifiers configured to identify a customized list of a set of annotation items; receiving a content item that includes audio information and video information, the audio information being time synchronized with the video information; extracting the audio information from the content item; determining an acoustic pattern that characterizes a portion of the audio information by performing audio analysis on the audio information, wherein the acoustic pattern is associated with a temporal location in the audio information; determining a corresponding temporal location in the video information based at least in part on the temporal location of the acoustic pattern; analyzing a portion of the video information at the corresponding temporal location using the set of classifiers to determine a set of matching vectors, individual matching vectors of the set of matching vectors including weighted annotation items from the customized list of the set of annotation items; determining a weighted summation of the weighted annotation items of the set of matching vectors to generate a merged matching vector; and selecting an annotation item from the customized list of the set of annotation items from the merged matching vector associated with a highest weight, wherein the annotation item characterizes the video information at the corresponding temporal location.