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To determine the important trends and issues in thousands of comments from customers and make strategic decisions about business operations, managers must go over these messages manually and try to make sense of them in a time consuming and tedious manner. There is an urgent need for technologies that help improve the efficiency of customer message management. We develop new issue identification techniques based on clustering and context aware similarity networks to enable managers to discover knowledge in text messages. We engineer a tool set specifically for exploring short text messages in the context of customer relationship management. In this paper, we report a proof of concept prototype called Message Sense Maker that can assist managers to map the overall sentiment of customers semiautomatically. We further justify the choice of particular technologies and validate our system through a field study of a customer support center in a large university.