Personalized generation of word clouds from tweets

Martin Leginus, Cheng Xiang Zhai, Peter Dolog

Research output: Contribution to journalArticlepeer-review


Active users of Twitter are often overwhelmed with the vast amount of tweets. In this work we attempt to help users browsing a large number of accumulated posts. We propose a personalized word cloud generation as a means for users' navigation. Various user past activities such as user published tweets, retweets, and seen but not retweeted tweets are leveraged for enhanced personalization of word clouds. The best personalization results are attained with user past retweets. However, users' own past tweets are not as useful as retweets for personalization. Negative preferences derived from seen but not retweeted tweets further enhance personalized word cloud generation. The ranking combination method outperforms the preranking approach and provides a general framework for combined ranking of various user past information for enhanced word cloud generation. To better capture subtle differences of generated word clouds, we propose an evaluation of word clouds with a mean average precision measure.

Original languageEnglish (US)
Pages (from-to)1021-1032
Number of pages12
JournalJournal of the Association for Information Science and Technology
Issue number5
StatePublished - May 1 2016


  • information filtering
  • information seeking
  • web mining

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences


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