TITLE

Word cloud segmentation for simplified exploration of trending topics on Twitter

AUTHOR(S)
Shahid, Nabila; Ilyas, Muhammad U.; Alowibdi, Jalal S.; Aljohani, Naif R.
PUB. DATE
October 2017
SOURCE
IET Software;2017, Vol. 11 Issue 5, p214
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
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 subtopics. 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.
ACCESSION #
125283027

 

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