Abstract
Representing and interpreting human opinions within an unstructured framework is inherently complex. Hesitant fuzzy linguistic term sets offer a comprehensive context that facilitates a nuanced understanding of diverse perspectives. This study introduces a methodology that integrates sentiment analysis with hesitant fuzzy linguistic term sets to effectively aggregate and compare news from diverse sources. By employing linguistic scales, our approach enhances the interpretation of various perceptions and attitudes, facilitating comprehensive knowledge extraction and representation. The main objective of this research is to conduct a comparative analysis of news coverage across European countries in relation to the Israel–Gaza war. This analysis aims to capture the multifaceted sensitivities surrounding the ongoing situation, highlighting how different nations perceive the conflict.
| Original language | English |
|---|---|
| Article number | 8 |
| Number of pages | 13 |
| Journal | Machine Learning and Knowledge Extraction |
| Volume | 7 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Mar 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 16 Peace, Justice and Strong Institutions
Keywords
- knowledge extraction
- knowledge representation
- linguistic modeling
- news aggregation
- sentiment analysis
- unbalanced hesitant fuzzy linguistic term sets
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