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access icon free Word cloud segmentation for simplified exploration of trending topics on Twitter

  • Author(s): Nabila Shahid 1 ; Muhammad U. Ilyas 1, 2 ; Jalal S. Alowibdi 2 ; Naif R. Aljohani 3
    • View affiliations
  • Source: Volume 11, Issue 5, October 2017, p. 214 – 220
    DOI:  10.1049/iet-sen.2016.0307 , Print ISSN 1751-8806, Online ISSN 1751-8814
© The Institution of Engineering and Technology
Received 19/12/2016, Accepted 21/04/2017, Revised 22/03/2017, Published 05/05/2017

Twitter is a popular microblogging platform, with 310 million monthly active users as of the first quarter of 2016. It is a rapidly growing microblogging platform where people share opinions, news on any topic of their interest. More than 7000 tweets are posted every second. Due to the enormous volume of data being generated, it becomes difficult to extract useful/meaningful information. Tweets collected from Twitter on a certain topic may consist of numerous conversation threads about relevant sub-topics. However, it is difficult to discern these sub-topics if the data is visualised as a single word cloud. The authors transform a corpus of tweets to a spectral domain and evaluate the results from a number of clustering algorithms, including K-means, latent semantic indexing and non-negative matrix factorisation to construct clustered word clouds that helps identify sub-topics under a broader topic.

Inspec keywords: cloud computing; pattern clustering; matrix decomposition; social networking (online); natural language processing; indexing

Other keywords: word cloud segmentation; trending topic exploration; nonnegative matrix factorisation; latent semantic indexing; microblogging platform; k-means; Twitter; clustering algorithms; clustered word clouds; spectral domain

Subjects: Data handling techniques; Algebra; Information networks; Information analysis and indexing; Natural language interfaces

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