C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Identifying and analyzing judgment opinions
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
CallAssist: helping call center agents in preference elicitation
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-automated logging of contact center telephone calls
Proceedings of the 17th ACM conference on Information and knowledge management
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Hierarchical service analytics for improving productivity in an enterprise service center
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Customer satisfaction is a very important indicator of how successful a contact center is at providing services to the customers. Contact centers typically conduct a manual survey with a randomly selected group of customers to measure customer satisfaction. Manual customer satisfaction surveys, however, provide limited values due to high cost and the time lapse between the service and the survey. In this paper, we demonstrate that it is possible to automatically measure customer satisfaction by analyzing call transcripts enabling companies to measure customer satisfaction for every call in near real-time. We have identified various features from multiple knowledge sources indicating prosodic, linguistic and behavioral aspects of the speakers, and built machine learning models that predict the degree of customer satisfaction with high accuracy. The machine learning algorithms used in this work include Decision Tree, Naive Bayes, Logistic Regression and Support Vector Machines (SVMs). Experiments were conducted for a 5-point satisfaction measurement and a 2-point satisfaction measurement using customer calls to an automotive company. The experimental results show that customer satisfaction can be measured quite accurately both at the end of calls and in the middle of calls. The best performing 5-point satisfaction classification yields an accuracy of 66.09% outperforming the DominantClass baseline by 15.16%. The best performing 2-point classification shows an accuracy of 89.42% and outperforms both the DominantClass baseline and the CSRJudgment baseline by 17.7% and 3.3% respectively. Furthermore, Decision Tree and SVMs achieve higher F-measure than the CSRJudgment baseline in identifying both satisfied customers and dissatisfied customers.