Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for segmenting unstructured text into salient portions and identifying sentiments expressed in each segment. In particular, the disclosed systems utilize a segmentation machine learning model to segment unstructured text into salient portions and a sentiment identifying machine learning model to identify sentiments for each segment. Additionally, the disclosed systems determine a topic for each segment and associate it with an emotion label, a sentiment label or a predicted action label. In one or more embodiments, based on the topic associated with the emotional label, sentiment label or predicted action label, the disclosed systems determine and perform additional actions.

Abstract: Systems and methods are described for analyzing one or more online shopping missions to determine an estimated stage of a user within the mission and to adjust content for display to the user based on the estimated stage of the user within the mission. The estimated stage of the user within the mission may be determined using a model, such as a machine learning model, that accepts as input signals corresponding to the online shopping mission. The signals may represent user interface interactions and/or other data relating to the user and the mission.