Automatic Identification of Important Segments and Expressions for Mining of Business-Oriented Conversations at Contact Centers

Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 458–467, Prague, June 2007. c⃝2007 Association for Computational Linguistics Automatic Identification of Important Segments and Expressions for Mining of Business-Oriented Conversations at Contact Centers Hironori Takeuchi∗, L Venkata Subramaniam†, Tetsuya Nasukawa∗, and Shourya Roy† ∗IBM Research, Tokyo Research Laboratory †IBM Research, India Research Laboratory Shimotsuruma 1623-14, Yamato-shi Institutional Area 4, Block-C, Vasant Kunj Kanagawa 2428502 Japan New Delhi 110070 India {hironori, nasukawa}@jp.ibm.com {lvsubram, rshourya}@in.ibm.com Abstract Textual records of business-oriented conver- sations between customers and agents need to be analyzed properly to acquire useful business insights that improve productivity. For such an analysis, it is critical to iden- tify appropriate textual segments and ex- pressions to focus on, especially when the textual data consists of complete transcripts, which are often lengthy and redundant. In this paper, we propose a method to iden- tify important segments from the conversa- tions by looking for changes in the accuracy of a categorizer designed to separate differ- ent business outcomes. We extract effective expressions from the important segments to define various viewpoints. In text mining a viewpoint defines the important associations between key entities and it is crucial that the correct viewpoints are identified. We show the effectiveness of the method by using real datasets from a car rental service center. 1 Introduction “Contact center” is a general term for customer ser- vice centers, help desks, and information phone lines. Many companies operate contact centers to sell their products, handle customer issues, and ad- dress product-related and services-related issues. In contact centers, analysts try to get insights for im- proving business processes from stored customer contact data. Gigabytes of customer contact records are produced every day in the form of audio record- ings of speech, transcripts, call summaries, email, etc. Though analysis by experts results in insights that are very deep and useful, such analysis usually covers only a very small (1-2%) fraction of the total call volume and yet requires significant workload. The demands for extracting trends and knowledge from the whole text data collection by using text mining technology, therefore, are increasing rapidly. In order to acquire valuable knowledge through text mining, it is generally critical to identify im- portant expressions to be monitored and compared within the textual data. For example, given a large collection of contact records at the contact center of a manufacturer, the analysis of expressions for products and expressions for problems often leads to business value by identifying specific problems in a specific product. If 30% of the contact records with expressions for a specific product such as “ABC” contain expressions about a specific trouble such as “cracked”, while the expressions about the same trouble appear in only 5% of the contact records for similar products, then it should be a clue that the product “ABC” may actually have a crack-related problem. An effective way to facilitate this type of analysis is to register important expressions in a lexicon …

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