Representing uncertainty through sentiment and stance visualizations: A survey

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.