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Mining from Open Answers in Questionnaire Data
- 1. Mining from Open Answers in Questionnaire Data KDD 01 San Francisco CA LISA Copyright ACM 2001 Hang Li* NEC Corporation [email_address] Kenji Yamanishi NEC Corporation [email_address]
- 2. Agenda Analysis of open answers Rule Analysis Classification Rules Association Rules Algorithm Correspondence Analysis Mining Result
- 3. Analysis of open answers Automatically summarize open answers Automatically mine useful information from open answers. Survey Analyzer system to analyze open answers (SA.) Two statistical learning Rule learning (Rule analysis) Correspondence Analysis
- 4. Rule Analysis – Classification Rules A number of categories containing a number of texts. Automatically acquire rules from the categorizes texts. Classify new texts on the basis of the acquired rules. SA View each analysis target as a category View open answers associated with the target as texts.
- 6. Rule Analysis – Algorithm (SC) SA learn classification rules or association rules by Stochastic Complexity (SC) MLD (Minimum Description Length) principle. Rectangles : 10 open answers Analysis target: T Some contain a specific word: W △ SC > 0 is positive, that is most likely to have given rise to the data.
- 8. Relationship between Rule analysis and Correspondence analysis Rule analysis Employs a conditional probability model: P(Y|X) Provides the facts in detail. (Table 2, 3). Correspondence analysis Employs a joint probability model: P(Y, X) Yields the entire structure. (Position map.) Y : analysis target X: words
- 9. Mining result With Car Data With Eye-drap Data With Beverage Data
- 10. Advantage of the mining system It is much faster and less costly way to summarize or mine from open questions. SA is the first system that can performing rule analysis and correspondence analysis. New statistical learning methodology base on Stochastic Complexity. SA has successfully been used in the mining of various types of questionnaire data.