Abstract
Social media provides a wealth of information regarding users' perspectives on issues, public figures and brands, but it can be a time-consuming and labor-intensive process to develop data pipelines in which those perspectives are encoded, and to build visualizations that illuminate important developments. This paper describes a system for quickly developing a model of the conversation around an issue on Twitter, and a flexible visualization system that allows analysts to interactively explore key facets of the analysis.
Cite
CITATION STYLE
Higgins, D., Heilman, M., Jelesnianska, A., & Ingersoll, K. (2016). An unsupervised system for visual exploration of Twitter conversations. In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2016 at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016 (pp. 60–65). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-0412
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