Multilingual fine-grained emotion detection for business intelligence

Start - End 
2017 - 2021 (ongoing)
Type 
Department(s) 
Department of Translation, Interpreting and Communication
Research Focus 
Research Period 

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Abstract

Despite the plethora of often visually very attractive social media monitoring tools currently available on the market, none of the existing solutions provide fine-grained insights to support reputation monitoring. The salient dimensions of an organization’s reputation may differ across stakeholders: employees, investors, or consumers, who are all interested in different corporate performance characteristics (e.g. financial performance for investors, workplace condition for potential employees, quality of products and services for customers, social responsibility for a wider public). To have a better understanding of how stakeholders in different countries respond to companies and other organizations, we investigate different emotion frameworks to model human emotions expressed online to support business intelligence. A learnable emotion framework will be We operationalized and implemented it in an automatic emotion detection system for reputation monitoring.

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Phd Student(s)