Open AccessWord cloud segmentation for simplified exploration of trending topics on Twitter
Author(s) -
Shahid Nabila,
Ilyas Muhammad U.,
Alowibdi Jalal S.,
Aljohani Naif R.
Publication year - 2017
Publication title -
iet software
Language(s) - English
Resource type - Journals
ISSN - 1751-8814
DOI - 10.1049/iet-sen.2016.0307
Subject(s) - tag cloud , computer science , microblogging , social media , cloud computing , word (group theory) , information retrieval , search engine indexing , cluster analysis , domain (mathematical analysis) , construct (python library) , conversation , latent semantic analysis , natural language processing , world wide web , artificial intelligence , visualization , linguistics , mathematical analysis , philosophy , mathematics , programming language , operating system
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.