Multidimensional Sentiment Cube Mining for Process Monitoring
DOI:
https://doi.org/10.48048/tis.2022.3682Keywords:
Multidimensional sentiment cube (MDSC), Aspect-based sentiment analysis, Life insurance, Business process management notation (BPMN), Association rules mining, Customer relationship management (CRM)Abstract
Process monitoring is essential for quality improvement because it is necessary to find the answers to which business issues need to be understood. In the era of social media, many critiques concern the business domain, including life insurance, which is one of the significant business sectors in Thailand. To utilize this useful cloud corpus for the business improvement process, we propose a novel methodology for process monitoring using the concept of multidimensional sentiment cube (MDSC) mining to raise usefulness with the business process model notation (BPMN). As the ability of MDC raise unlimited analysis perspectives merge with sentiment analysis (MDSC), this method can provide more sets of data for association rules mining and meet the needs to be analyzed. The cube analysis scenario, which uses association rules mining results, can reveal a significant hidden issue among aspects and sub-aspects associated under our design with their measurements. The results can be used for monitoring, which presents the customer's sentiment from social media in the real business case and identifying in the real process model.
HIGHLIGHTS
- A methodology for process monitoring using the concept of multidimensional sentiment cube (MDSC) mining raise the usefulness of the business process model notation (BPMN) by utilizing the corpus for the business improvement process
- This method can provide more sets of data for association rules mining as the ability of MDC raise unlimited analysis perspectives to merge with sentiment analysis (MDSC)
- The results can monitor the customer’s sentiment from social media in the real business case and identify in the real process model
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