Abstract
Survey coding is the process of analyzing text responses to open-ended questions in surveys. We present SurveyCoder, a research prototype which helps the survey analysts to achieve significant automation of the survey coding process. SurveyCoder’s multi-label text classification algorithm makes use of a knowledge base that consists of linguistic resources, historical data, domain specific rules and constraints. Our method is applicable to surveys carried out in different domains.
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References
Giorgetti, D., Sebastiani, F.: Multiclass text categorization for automated survey coding. In: Proceedings of ACM Symposium on Applied Computing (SAC) (2003)
Esuli, A., Sebastiani, F.: Active learning strategies for multi-label text classification. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 102–113. Springer, Heidelberg (2009)
Esuli, A., Sebastiani, F.: Machines that learn how to code open-ended survey data. International Journal of Market Research 52(6) (2010)
Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press (1998)
Buchanan, B., Shortliffe, E.: Rule Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading (1984) ISBN 978-0-201-10172-0
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Patil, S., Palshikar, G.K. (2013). SurveyCoder: A System for Classification of Survey Responses. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2013. Lecture Notes in Computer Science, vol 7934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38824-8_52
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DOI: https://doi.org/10.1007/978-3-642-38824-8_52
Publisher Name: Springer, Berlin, Heidelberg
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