ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatic analysis of call-center conversations
Proceedings of the 14th ACM international conference on Information and knowledge management
Automatic generation of domain models for call centers from noisy transcriptions
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Automated quality monitoring for call centers using speech and NLP technologies
NAACL-Demonstrations '06 Proceedings of the 2006 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume: demonstrations
Mining conversational text for procedures with applications in contact centers
International Journal on Document Analysis and Recognition
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
A survey on sentiment detection of reviews
Expert Systems with Applications: An International Journal
Automatically training a problematic dialogue predictor for a spoken dialogue system
Journal of Artificial Intelligence Research
Impact of spontaneous speech features on business concept detection: a study of call-centre data.
Proceedings of the 2010 international workshop on Searching spontaneous conversational speech
Lexicon-based methods for sentiment analysis
Computational Linguistics
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In order to assure and to improve the quality of service, call center operators need to automatically identify the problematic calls in the mass of information flowing through the call center. Our method to select and rank those critical conversations uses linguistic text mining to detect sentiment markers on French automatic speech transcripts. The markers' weight and orientation are used to calculate the semantic orientation of the speech turns. The course of a conversation can then be graphically represented with positive and negative curves. We have established and evaluated on a manually annotated corpus three heuristics for the automatic selection of problematic conversations. Two proved to be very useful and complementary for the retrieval of conversations having segments with anger and tension. Their precision is high enough for use in real world systems and the ranking evaluated by mean precision follows the usual relevance behavior of a search engine.