Expert Opinion Extraction from a Biomedical Database
This work addresses the extraction of expert opinions from biomedical data to evaluate reliability, but it appears incremental as it builds on existing opinion mining concepts.
The paper tackled the problem of extracting frequent opinions from uncertain databases by introducing a new algorithm called OpMiner, which showed better quality patterns compared to literature-based methods in a biomedical database application.
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.