Classifying unstructed textual data using the Product Score Model: an alternative text mining algorithm

He, Q. and Veldkamp, B.P. (2012) Classifying unstructed textual data using the Product Score Model: an alternative text mining algorithm. In: Psychometrics in practice at RCEC. RCEC, Enschede, 47 - 62. ISBN 9789036533744

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Abstract:	Unstructured textual data such as students’ essays and life narratives can provide helpful information in educational and psychological measurement, but often contain irregularities and ambiguities, which creates difficulties in analysis. Text mining techniques that seek to extract useful information from textual data sources through identifying interesting patterns are promising. This chapter describes the general procedures of text classification using text mining and presents an alternative machine learning algorithm for text classification, named the product score model (PSM). Using the bag-of-words representation (single words), we conducted a comparative study between PSM and two commonly used classification models, decision tree and naïve Bayes. An application of these three models is illustrated for real textual data. The results showed the PSM performed the most efficiently and stably in classifying text. Implications of these results for the PSM are further discussed and recommendations about its use are given
Item Type:	Book Section
Copyright:	© RCEC, Cito/University of Twente, Enschede, Netherlands, 2012
Faculty:	Behavioural Sciences (BS)
Research Group:	Research Methodology, Measurement and Data Analysis (OMD)
Link to this item:	http://purl.utwente.nl/publications/80208
Official URL:	http://dx.doi.org/10.3990/3.9789036533744.ch5
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