Abstract
Data mining is an increasing area of interest where the collection of large amount of data is characterized by heterogeneous information with respect to origin and content; thus, a high degree of specialization is required for a correct analysis. In this paper, we limit ourselves to consider opinions that are expressed as ordered preferences and may be delivered as rating or ranking evaluations. Such situations are different and deserve careful considerations. In both cases, we discuss the framework of CUB models introduced to analyse the ordinal responses by which people express their opinions. Specifically, the approach may be inserted as a useful routine in data mining area for improving the study of essential features supported by empirical evidence.
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Notes
- 1.
Such optimizations have been pursued by the application module CML of GAUSS language (Aptech, 2002), aimed at constrained maximum likelihood estimation.
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Acknowledgements
The paper has been partly supported by a MIUR grant (code 2008WKHJPK-PRIN2008) for the project: “Modelli per variabili latenti basati su dati ordinali: metodi statistici ed evidenze empiriche” within the Research Unit of University of Naples Federico II (PUC number E61J10000020001).
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Iannario, M., Piccolo, D. (2013). A Model-Based Approach for Qualitative Assessment in Opinion Mining. In: Giusti, A., Ritter, G., Vichi, M. (eds) Classification and Data Mining. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28894-4_14
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