COBRA - Mining Web for Corporate Brand and Reputation Analysis

Experimental results show that with a much small labeled corpus cross-training could produce both product properties and opinion words which are very close to what Naive Bayesian Classifiers could do with a large labeled corpus.

Abstract

Corporations are extremely sensitive to issues such as brand stewardship and product reputation. Traditional brand image and reputation tracking is limited to news wires and contact centres analysis. However, with the emergence of Web, consumer generated media (COM), such as blogs, news forums, message boards, and Web pages/sites, is rapidly becoming the "voice of the people". This paper describes a COBRA (corporate brand and reputation analysis) solution that mines a wide range of COM contents for brand and reputation analysis. The solution contains a flexible ETL (Extract, Transform, and Load) engine that processes diverse sets of structured and unstructured information, a suite of analytical capabilities that mines COM content to extract semantic entities and insights out of the data, and an alerting mechanism that utilizes the analytics results to accurately generate brand and reputation alerts. We use a real-world case study to demonstrate the effectiveness of our approach.