An inductive rule learning technique for next mining in questionnaires
This paper describes an inductive rule learning (IRL) technique for classifying questionnaires based on the natural language responses to the open-ended questions frequently found in questionnaire data.These responses are deemed to provide important information to the purpose of the questionnaire.G...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/12032/1/PID23.pdf |
| Summary: | This paper describes an inductive rule learning (IRL) technique for classifying questionnaires based on the natural language
responses to the open-ended questions frequently found in questionnaire data.These responses are deemed to provide important information to the purpose of the questionnaire.Given that the responses are in the form of unstructured natural language text and that a collection of questionnaires can comprise thousands of returns, an automated approach for handling such text is desirable for analysis purposes.One common analysis task is the classification of questionnaires.For this purpose, an IRL technique is presented.An empirical comparison is also conducted to compare the
presented technique with other established machine learning techniques.This IRL technique has been shown to be effective and efficient when applied to the classification of a collection of veterinary questionnaires. |
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