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

Chapter
Part of the Studies in Computational Intelligence book series (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.

Keywords

Automated summarization Clustering Data mining Twitter Social networks Keyword extraction Topic modeling 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghUSA
  2. 2.Advanced Technology LabsAdobe Systems IncSan JoseUSA

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