{"title":"Representing uncertainty through sentiment and stance visualizations: A survey","authors":"Bárbara Ramalho, Joaquim Jorge, Sandra Gama","doi":"10.1016/j.gmod.2023.101191","DOIUrl":null,"url":null,"abstract":"<div><p>Visual analytics combines automated analysis techniques with interactive visualizations for effective understanding, reasoning, and decision-making on complex data. However, accurately classifying sentiments and stances in sentiment analysis remains challenging due to ambiguity and individual differences. This survey examines 35 papers published between 2016 and 2022, identifying unaddressed sources of friction that contribute to a gap between individual sentiment, processed data, and visual representation. We explore the impact of visualizations on data perception, analyze existing techniques, and investigate the many facets of uncertainty in sentiment and stance visualizations. We also discuss the evaluation methods used and present opportunities for future research. Our work addresses a gap in previous surveys by focusing on uncertainty and the visualization of sentiment and stance, providing valuable insights for researchers in graphical models, computational methods, and information visualization.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"129 ","pages":"Article 101191"},"PeriodicalIF":2.5000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Graphical Models","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1524070323000218","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Visual analytics combines automated analysis techniques with interactive visualizations for effective understanding, reasoning, and decision-making on complex data. However, accurately classifying sentiments and stances in sentiment analysis remains challenging due to ambiguity and individual differences. This survey examines 35 papers published between 2016 and 2022, identifying unaddressed sources of friction that contribute to a gap between individual sentiment, processed data, and visual representation. We explore the impact of visualizations on data perception, analyze existing techniques, and investigate the many facets of uncertainty in sentiment and stance visualizations. We also discuss the evaluation methods used and present opportunities for future research. Our work addresses a gap in previous surveys by focusing on uncertainty and the visualization of sentiment and stance, providing valuable insights for researchers in graphical models, computational methods, and information visualization.
期刊介绍：
Graphical Models is recognized internationally as a highly rated, top tier journal and is focused on the creation, geometric processing, animation, and visualization of graphical models and on their applications in engineering, science, culture, and entertainment. GMOD provides its readers with thoroughly reviewed and carefully selected papers that disseminate exciting innovations, that teach rigorous theoretical foundations, that propose robust and efficient solutions, or that describe ambitious systems or applications in a variety of topics.
We invite papers in five categories: research (contributions of novel theoretical or practical approaches or solutions), survey (opinionated views of the state-of-the-art and challenges in a specific topic), system (the architecture and implementation details of an innovative architecture for a complete system that supports model/animation design, acquisition, analysis, visualization?), application (description of a novel application of know techniques and evaluation of its impact), or lecture (an elegant and inspiring perspective on previously published results that clarifies them and teaches them in a new way).
GMOD offers its authors an accelerated review, feedback from experts in the field, immediate online publication of accepted papers, no restriction on color and length (when justified by the content) in the online version, and a broad promotion of published papers. A prestigious group of editors selected from among the premier international researchers in their fields oversees the review process.
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