Advertisement

A Model-Based Approach for Qualitative Assessment in Opinion Mining

  • Maria Iannario
  • Domenico Piccolo
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Ordinal Data Multivariate Parameter Permutation Vector Italian Newspaper Multivariate Random Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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).

References

  1. Aptech Systems, Inc. (2002). Constrained maximum likelihood estimation for GAUSS, version 2.0.3. Mapley Valley, WA.Google Scholar
  2. Chen, Z. (2001). Data mining and uncertain reasoning: An integrated approach. New York: Wiley.Google Scholar
  3. Corduas, M., Iannario, M., & Piccolo, D. (2010). A class of statistical models for evaluating services and performances. In M. Bini, et al. (Eds.), Statistical methods for the evaluation of educational services and quality of products (Contribution to Statistics, pp. 99–117). New York: Springer.Google Scholar
  4. D’Elia, A., & Piccolo, D. (2005). A mixture model for preference data analysis. Computational Statistics & Data Analysis, 49, 917–934.MathSciNetzbMATHCrossRefGoogle Scholar
  5. Fayyad, U., Piatelsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining. Cambridge: AAAI/MIT.Google Scholar
  6. Frawley, W., Piatelsky-Shapiro, G., & Matheus, C. (1991). Knowledge discovery in databases: An overview. In G. Piatetsky-Shapiro & W. Frawley (Eds.), Knowledge discovery in databases (pp. 1–30). Menlo Park: AAAI/MIT. Reprinted also in: AI Magazin, Fall (1992).Google Scholar
  7. Iannario, M. (2008). A class of models for ordinal variables with covariates effects. Quaderni di Statistica, 10, 53–72.Google Scholar
  8. Iannario, M. (2010). On the identifiability of a mixture model for ordinal data. Metron, LXVIII, 87–94.Google Scholar
  9. Iannario, M. (2012). Preliminary estimators for a mixture model of ordinal data, Advances in Data Analysis and Applications, 6, DOI 10.1007/s11634-012-0111-5.Google Scholar
  10. Iannario, M., & Piccolo, D. (2009). A program in R for CUB models inference, Version 2.0, Available at http://www.dipstat.unina.it/CUBmodels1/.
  11. Iannario, M., & Piccolo, D. (2010). Statistical modelling of subjective survival probabilities. GENUS, LXVI, 17–42.Google Scholar
  12. Iannario, M., & Piccolo, D. (2012). CUB models: Statistical methods and empirical evidence. In R. Kenett & S. Salini (Eds.), Modern analysis of customer satisfaction surveys: with applications using R (pp. 231–258) Chichester: Wiley.Google Scholar
  13. Liu, B. (2010). Sentiment analysis and subjectivity. In N. Indurkhya & F. J. Damerau (Eds.), Handbook of natural language processing (pp. 627–666). London: Chapman and Hall.Google Scholar
  14. Marden, J. I. (1995). Analyzing and modeling rank data. London: Chapman and Hall.zbMATHGoogle Scholar
  15. McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). London: Chapman and Hall.zbMATHGoogle Scholar
  16. Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of statistical analysis and data mining applications. Amsterdam: Elsevier/Academic.zbMATHGoogle Scholar
  17. Piccolo, D. (2003). On the moments of a mixture of uniform and shifted binomial random variables. Quaderni di Statistica, 5, 85–104.Google Scholar
  18. Piccolo, D. (2006). Observed information matrix for MUB models. Quaderni di Statistica, 8, 33–78.Google Scholar
  19. Piccolo, D., & D’Elia, A. (2008). A new approach for modelling consumers’ preferences. Food Quality and Preference, 19, 247–259.CrossRefGoogle Scholar
  20. Plackett, R. L. (1975). The analysis of permutations. Applied Statistics, 24, 193–202.MathSciNetCrossRefGoogle Scholar
  21. Xu, L. (2000). A multistage ranking model. Psychometrika, 65, 217–231.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Statistical SciencesUniversity of Naples Federico IINaplesItaly

Personalised recommendations