Abstract: A content intelligence module implementing a unique polarization algorithm may operate to identify products with polarized sentiments in user-generated content. The module may be configured to, at a first computer: calculate an overall average rating on a product across segments in a dimension; for each segment in the dimension, calculate a segment average rating, a total number of ratings, a segment distance, and a weighted variation score; calculate a total weighted variation score for the dimension; analyze a distribution of overall sentiment variation in the dimension; set a polarization score for the dimension utilizing the total weighted variation score; and repeat the above steps for each feature, each product, and each dimension to produce polarization scores. Visual representations corresponding to the polarization scores may be presented through a browser application running on a second computer communicatively connected to the first computer over a network connection.

Abstract: An overall average review rating for a product may be determined, based on user ratings that are associated with opinions of a product, within a dimension corresponding to a user trait. A segment variation score for each of a plurality of segments of the dimension may be determined. Each segment may correspond to one or more values of the user trait corresponding to the dimension. A total variation score may be determined for the dimension based on the segment variation scores determined for each of the plurality of segments of the dimension. The total variation score for the dimension may be compared to a polarization threshold to determine whether the dimension is polarized. Generated information may identify the product as having sentiment that is polarized.