Analysis on the Usage of Topic Model with Background Knowledge inside Discussion Activity in Industrial Engineering Context

Abstract

Consensus building process for enterprise digital transformation is a significant approach on the implementation of Internet of Things (IoT) solutions through product lifecycle management (PLM). When we improve the consensus building process, it is important to find any latent opinions and hidden dialog patterns analyzing discussion activities by stakeholders. Several approaches have been proposed in forms of instructions and frameworks such as causal model of Consensus Building Theory (CBT) and short-term intensive workshop in strategy planning phase of Product Lifecycle Management (PLM) process. This paper will analyze a new approach to improve consensus building process by summarizing discussion activity. The proposed method is done by performing data augmentation and topic modeling with the help of background knowledge on discussion activity held within industrial engineering context. Our method produces a complete summarization of discussion activity that consists of topic distribution and distribution similarity between topics. We also found that the usage of data augmentation and background knowledge will improve topic quality. We validate our findings to a professional consultant and conclude that our approach gives an adequate contribution towards summarizing discussion activity that might improve consensus building process.