A Learning Criterion for Stochastic Rules
Machine Learning - Computational learning theory
Text mining: finding nuggets in mountains of textual data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Monitoring a newsfeed for hot topics
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting significant time varying features from text
Proceedings of the eighth international conference on Information and knowledge management
Text classification using ESC-based stochastic decision lists
Proceedings of the eighth international conference on Information and knowledge management
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Topic analysis using a finite mixture model
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Fisher information and stochastic complexity
IEEE Transactions on Information Theory
A decision-theoretic extension of stochastic complexity and its applications to learning
IEEE Transactions on Information Theory
Mining Open Answers in Questionnaire Data
IEEE Intelligent Systems
Mining product reputations on the Web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Sentiment Mining in WebFountain
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Using data mining as a strategy for assessing asynchronous discussion forums
Computers & Education
Promotion of self-assessment for learners in online discussion using the visualization software
CSCL '05 Proceedings of th 2005 conference on Computer support for collaborative learning: learning 2005: the next 10 years!
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
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Opinion comparison between internet forums and customer reviews
International Journal of Computer Applications in Technology
Dealing with open-answer questions in a peer-assessment environment
ICWL'12 Proceedings of the 11th international conference on Advances in Web-Based Learning
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Surveys are an important part of marketing and customer relationship management, and open answers (i.e., answers to open questions) in particular may contain valuable information and provide an important basis for making business decisions. We have developed a text mining system that provides a new way for analyzing open answers in questionnaire data. The product is able to perform the following two functions: (A) accurate extraction of characteristics for individual analysis targets, (B) accurate extraction of the relationships among characteristics of analysis targets. In this paper, we describe the working of our text mining system. It employs two statistical learning techniques: rule analysis and Correspondence Analysis for performing the two functions. Our text mining system has already been put into use by a number of large corporations in Japan in the performance of text mining on various types of survey data, including open answers about brand images, open answers about company images, complaints about products, comments written on home pages, business reports, and help desk records. In this it has been found to be useful in forming a basis for effective business decisions.