Abstract: A disclosed sentiment topic modeling tool identifies issues within voluminous customer review data, based on particular categories of review submission (e.g., particular products and experiences) and concern areas (e.g., quality, performance, suitability of features), and abstracts the extracted topic data into a manageable set of focus areas for business operations improvements. An exemplary process includes: receiving a plurality of reviews; selecting a category of review to use for a topic network; selecting a number of topics for generating the topic network; generating, based at least on the selected category and the selected number of topics, the topic network; generating a plurality of topic networks in a topic network group, and determining a set of themes for the group. Additional network groups are generated, and a set of themes is determined for each. A set of focus areas is determined, based at least on the sets, and reports are generated.

Abstract: A disclosed sentiment topic modeling tool identifies issues within voluminous customer review data, based on particular categories of review submission (e.g., particular products and experiences) and concern areas (e.g., quality, performance, suitability of features), and abstracts the extracted topic data into a manageable set of focus areas for business operations improvements. An exemplary process includes: receiving a plurality of reviews; selecting a category of review to use for a topic network; selecting a number of topics for generating the topic network; generating, based at least on the selected category and the selected number of topics, the topic network; generating a plurality of topic networks in a topic network group, and determining a set of themes for the group. Additional network groups are generated, and a set of themes is determined for each. A set of focus areas is determined, based at least on the sets, and reports are generated.

Abstract: Examples provide a system for performing retail-based cost reverse engineering and cost comparison within item similarity clusters for cost negotiations associated with a plurality of items. A cost manager component creates item similarity clusters based on item descriptions and cost trends associated with items over time. The cost manager component identifies items for action recommendations based on quantity and margin for each item. The recommendations can include a recommended set of item suitable for negotiations, promotional activities and/or a set of substitute items recommended for replacement of a selected item in an item assortment.