Analysts and social scientists in the humanities and industry require techniques to help visualize large quantities of microblogging data. Methods for the automated analysis of large scale social media data (on the order of tens of millions of tweets) are widely available, but few visualization techniques exist to support interactive exploration of the results. In this paper, we present extended descriptions of ThemeCrowds and SentireCrowds, two tag-based visualization techniques for this data. We subsequently introduce a new list equivalent for both of these techniques and present a number of case studies showing them in operation. Finally, we present a formal user study to evaluate the effectiveness of these list interface equivalents when comparing them to ThemeCrowds and SentireCrowds. We find that discovering topics associated with areas of strong positive or negative sentiment is faster when using a list interface. In terms of user preference, multilevel tag clouds were found to be more enjoyable to use. Despite both interfaces being usable for all tested tasks, we have evidence to support that list interfaces can be more efficient for tasks when an appropriate ordering is known beforehand.
A Study on Herd Behavior Using Sentiment Analysis in Online Social Network
Amity University Kolkata（阿米提大学加尔各答分校）；IBM India Private Limited（IBM印度私人有限公司）；Jalpaiguri Government Engineering College（贾尔派古里政府工程学院）；Netaji Subhas Open University（内塔吉·苏巴斯开放大学）
Leveraging Twitter Data for Sentiment Analysis of Transit User Feedback: An NLP Framework
Department of Civil and Environmental Engineering（土木与环境工程系）；The Pennsylvania State University（宾夕法尼亚州立大学）；College of Information Sciences and Technology（信息科学与技术学院）
Decision Making For Celebrity Branding: An Opinion Mining Approach Based On Polarity And Sentiment Analysis Using Twitter Consumer-Generated Content (CGC)
Shahid Beheshti University（沙希德·贝赫什蒂大学）；University of Tehran（德黑兰大学）；Amirkabir University of Technology（阿米卡比尔理工大学）
Exploring Agent Interactions in MoltBook through Social Network Analysis
National University of Kaohsiung（高雄大学）；Kindai University（近畿大学）；Seinäjoki University of Applied Sciences（塞伊奈约基应用科学大学）；National Taipei University of Technology（台北科技大学）
What are People Talking about in #BlackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerging in Online Social Movements through the Latent Dirichlet Allocation Model
Duke Kunshan University（杜克-昆山大学）；Data Science Research Center（数据科学研究中心）；Social Science Division Duke Kunshan University（社会科学系杜克-昆山大学）
