Understanding the role of sentiment analysis in contract risk classification

Abstract

This paper describes a novel approach for identifying IT outsourcing contract renewal risk ahead of contract expiration by taking into account not only client satisfaction survey results (in the form of numeric scores), but also client interview transcripts (in the form of unstructured text). By using machine learning techniques, the interview transcripts are automatically processed to identify important topics of interest along with an associated sentiment for each topic. The output of the sentiment analysis is then used as an input (in addition to client satisfaction survey scores) to classify contract renewal risk. We show that, by using sentiment analysis to transform unstructured textual information into structured input, the classification accuracy of non-renewing contracts, in particular, is substantially enhanced. Moreover, the topics with negative sentiments can shed light on the root causes of problems leading to contract non-renewal. © 2014 IEEE.