Abstract
In this paper, we tackle the problem of extracting frequent opinions from uncertain databases. We introduce the foundation of an opinion mining approach with the definition of pattern and support measure. The support measure is derived from the commitment definition. A new algorithm called OpMiner that extracts the set of frequent opinions modelled as a mass functions is detailed. Finally, we apply our approach on a real-world biomedical database that stores opinions of experts to evaluate the reliability level of biomedical data. Performance analysis showed a better quality patterns for our proposed model in comparison with literature-based methods.
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Notes
- 1.
A pattern is called frequent if its computed support (i.e. frequency in the database) is higher than or equal to a fixed threshold set by an expert.
- 2.
A BBA is called a certain BBA when it has one focal element, which is a singleton. It is representative of perfect knowledge and the absolute certainty.
- 3.
A BBA is said to be simple if it has at most two focal sets and, if it has two, \(\varTheta \) is one of those.
- 4.
A BBA is said to be normal if \(\emptyset \) is not a focal set.
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Acknowledgements
This work is a part of the PEPS project funded by the French national agency for medicines and health products safety (ANSM), and of the SePaDec project funded by Region Bretagne.
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Samet, A., Guyet, T., Negrevergne, B., Dao, TT., Hoang, T.N., Tho, M.C.H.B. (2017). Expert Opinion Extraction from a Biomedical Database. In: Antonucci, A., Cholvy, L., Papini, O. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017. Lecture Notes in Computer Science(), vol 10369. Springer, Cham. https://doi.org/10.1007/978-3-319-61581-3_13
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