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Computational Framework for Generating Visual Summaries of Topical Clusters in Twitter Streams

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Social Networks: A Framework of Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 526))

Abstract

As a huge amount of tweets become available online, it has become an opportunity and a challenge to extract useful information from tweets for various purposes. This chapter proposes a novel way to extract topical structure from a large set of tweets and generate a usable summarization along with related topical keywords. Our system covers the full span of the topical analytics of tweets starting with collecting the tweets, processing and preparing them for text analysis, forming clusters of relevant words, and generating visual summaries of most relevant keywords along with their topical context. We evaluate our system by conducting a user study and the results suggest that users are able to detect relevant information and infer relationships between keywords better with our summarization method than they do with the commonly used word cloud visualizations.

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Author information

Authors and Affiliations

  1. Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Miray Kas

  2. Advanced Technology Labs, Adobe Systems Inc, San Jose, CA, 95110, USA

    Bongwon Suh

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  1. Miray Kas
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  2. Bongwon Suh
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Correspondence to Miray Kas .

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Editors and Affiliations

  1. Electrical & Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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Kas, M., Suh, B. (2014). Computational Framework for Generating Visual Summaries of Topical Clusters in Twitter Streams. In: Pedrycz, W., Chen, SM. (eds) Social Networks: A Framework of Computational Intelligence. Studies in Computational Intelligence, vol 526. Springer, Cham. https://doi.org/10.1007/978-3-319-02993-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-02993-1_9

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  • Online ISBN: 978-3-319-02993-1

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