Expert Opinion Extraction from a Biomedical Database

09/11/2017
by   Ahmed Samet, et al.
0

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.

READ FULL TEXT
research
12/20/2022

A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach

Opinion mining is the branch of computation that deals with opinions, ap...
research
10/12/2019

Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data

Subjective Logic (SL) is one of well-known belief models that can explic...
research
08/28/2022

Opinion Leader Detection in Online Social Networks Based on Output and Input Links

The understanding of how users in a network update their opinions based ...
research
12/19/2019

Fast Mining of Spatial Frequent Wordset from Social Database

In this paper, we propose an algorithm that extracts spatial frequent pa...
research
03/27/2002

The Algorithms of Updating Sequential Patterns

Because the data being mined in the temporal database will evolve with t...
research
12/04/2021

Utilizing Expert Opinion to inform Extrapolation of Survival Models

In decision modelling with time to event data, there are a variety of pa...
research
01/27/2020

Towards Quantifying the Distance between Opinions

Increasingly, critical decisions in public policy, governance, and busin...

Please sign up or login with your details

Forgot password? Click here to reset