Abstract
Topic models are regularly used to provide directed exploration and a high-level overview of a corpus of unstructured text. In many cases, it is important to analyze the evolution of topics over a time range. In this work, we present an application of statistical topic modeling and alignment (binned topic models) to group related documents into automatically generated topics and align the topics across a time range. Additionally, we present TopicFlow , an interactive tool to visualize the evolution of these topics. The tool was developed using an iterative design process based on feedback from expert reviewers. We demonstrate the utility of the tool with a detailed analysis of a corpus of data collected over the period of an academic conference, and demonstrate the effectiveness of this visualization for reasoning about large data by a usability study with 18 participants.
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Notes
- 1.
This work is an extension of our prior work [13], in which we originally introduced TopicFlow as a Twitter analysis tool. A video demonstrating this work can be found here: https://www.youtube.com/watch?v=qqIlvMOQaOE&feature=youtu.be
- 2.
For TopicFlow, the number of topics is adjustable with a default of 15 to balance granularity and comprehensibility of the resulting topics.
- 3.
For this implementation the LDA algorithm runs for 100 iterations with \(\alpha =0.5\) and \(\beta =0.5\).
- 4.
For example, Twitter-specific stop words include {rt, retweet, etc.} and Spanish stop words include {el, la, tu, etc.}.
- 5.
\(cos(A, B) = \frac{A\cdot B}{\left\| A \right\| \left\| B \right\| }\).
- 6.
For prototyping and evaluation purposes, the threshold was set between 0.15 and 0.25 depending on the dataset.
- 7.
A prototype of the TopicFlow tool is available for demo here: http://www.cs.umd.edu/~maliks/topicflow/TopicFlow.html.
- 8.
Twitter’s open API and the fact that tweets are rich with metadata, specifically time stamps, makes it an appropriate data source for prototyping and testing.
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Acknowledgments
We would like to thank Timothy Hawes, Cody Dunne, Marc Smith, Jimmy Lin, Jordan Boyd-Graber, Catherine Plaisant, Peter David, and Jim Nolan for their input throughout the design and implementation of this project and thoughtful reviews of this paper. Additionally, we would like to thank Jianyu (Leo) Li and Panagis Papadatos for their assistance in designing, developing, and evaluating the initial version of the tool.
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Smith, A., Malik, S., Shneiderman, B. (2015). Visual Analysis of Topical Evolution in Unstructured Text: Design and Evaluation of TopicFlow. In: Kazienko, P., Chawla, N. (eds) Applications of Social Media and Social Network Analysis. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-19003-7_9
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