Walaa Abuasaker

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.

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, thus 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 explore the different perceptions and attitudes surrounding the ongoing situation, highlighting how different nations perceive the conflict.

In this study, we propose a methodology using hesitant fuzzy linguistic term sets to aggregate rating values. The main idea is to define an integrated methodology for collaborative platforms involving individuals that use the same set of linguistic terms with different semantics. The objective is to fuse the opinions of different profiles of reviewers or customers when assessing an alternative. The proposed approach allows considering previous assessments to give personalized meaning to a set of linguistic terms. To this end, the concepts of linguistic perceptual maps and projections among perceptual maps are introduced to model and aggregate reviewers' different profiles. Finally, we compute a measure of consensus to capture the agreement among reviewers' opinions. All these concepts are applied in a real case example of rating books from Amazon to demonstrate the potential of the methodology in facilitating the evaluation and comparison of different alternatives. Using our methodology, the degree of consensus among customers can be used to rate in a more precise way the books before recommendation.