SentiSand: Visual Storytelling of Individual Sentiments on Social Media

Utilizing visualization techniques for opinion mining and sentiment exploration of social media texts is a pivotal concern in the visualization field. Research predominantly concentrates on the collective expression of numerous users on popular topics within the public domain. However, there is a need to focus on changing or evolving sentiments for individuals. This paper tries to integrate sentiment analysis with visualization techniques to enable individual users to visually explore the underlying sentiment stories behind their social media texts. Therefore, the SentiSand system is designed and developed based on user requirements. It employs innovative visual design to transform the text from users' social media into a series of visual stories to facilitate users tracking their sentiment changes. The system provides the functionalities of visualizing the overview of the sentiment evolution over a specified period, the sentiment polarity distribution, and words used over that period as well. In a user study, eighteen participants with diverse backgrounds are invited to evaluate the system. The results demonstrate that SentiSand can effectively and intuitively depict sentiment states and changes. All participants gave the system high ratings across dimensions of creativity, practicality, and narrative coherence. Overall, this paper introduces a novel perspective and methodology for visualizing personal sentiments, thereby contributing to an enhanced comprehension of and intervention in individual sentiment states.