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Machine Learning - Special issue on information retrieval
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Machine Learning - Special issue on information retrieval
Information Retrieval
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The VLDB Journal — The International Journal on Very Large Data Bases
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ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
An integrated system for automatic customer satisfaction analysis in the services industry
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
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Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Text classification has matured as a research discipline over the last decade. Independently, business intelligence over structured databases has long been a source of insights for enterprises. In this work, we bring the two together for Customer Satisfaction(C-Sat) analysis in the services industry. We present ITACS, a solution combining text classification and business intelligence integrated with a novel interactive text labeling interface. ITACS has been deployed in multiple client accounts in contact centers. It can be extended to any services industry setting to analyze unstructured text data and derive operational and business insights. We highlight importance of interactivity in real-life text classification settings. We bring out some unique research challenges about label-sets, measuring accuracy, and interpretability that need serious attention in both academic and industrial research. We recount invaluable experiences and lessons learned as data mining researchers working toward seeing research technology deployed in the services industry.