Deployment of Semantic Social Media Analysis to Call Center

The number of inquiries to call centers regarding product malfunctions has been increasing in recent years, and thus manufacturers are struggling with their responses. The Consumer Affairs Agency in Japan stated that the initial response to an inquiry is especially important, since flaming directed toward the company may immediately occur on the Web, and may greatly affect the reputation and sales of the product if the response is inappropriate. However, when a call center accepts the first inquiry, an operator cannot determine whether the malfunction is due to a problem of a model or to a user’s way of using the product. Therefore, we have developed a system to automatically determine if the inquiry content is the tip of an iceberg by graph-matching the inquiry content to a Linked Data network, which represents the reputation information of a product on social media. Moreover, by tracing causal links in the network, the system also determines if the inquiry is connecting to users’ dissatisfaction and discontent, and then notifies the inquiry to a quality control section with high priority to prevent flaming. In this paper, we first present our approach for converting social media information to Linked Data, and show that an experiment achieved 94% accuracy. We also explain the matching between the inquiry content and Linked Data and its accuracy, and a method of extracting the causal link to the users’ complaints.