Article In: orcid, dblp, scopus, cienciavitae
Representing uncertainty through sentiment and stance visualizations: A survey
A survey
Graphical Models
— 2023 — Elsevier
Key information
Authors:
Published in
09/01/2023
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.
Publication details
Authors in the community:
Bárbara de Araújo Ramalho
ist182057
Joaquim Armando Pires Jorge
ist13909
Sandra Pereira Gama
ist152404
Publication version
AO - Author's Original
Publisher
Elsevier
Title of the publication container
Graphical Models
First page or article number
101191
Last page
101191
Volume
129
ISSN
1524-0703
Fields of Science and Technology (FOS)
computer-and-information-sciences - Computer and information sciences
Publication language (ISO code)
eng - English
Alternative identifier (URI)
http://dx.doi.org/10.1016/j.gmod.2023.101191
Rights type:
Only metadata available
Financing entity
United Nations Educational, Scientific and Cultural Organization
Identifier for the funding entity: 2022.09212