The Royal birth of 2013: analysing and visualising public sentiment in the UK using Twitter

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)


Analysis of information retrieved from microblogging services such as Twitter can provide valuable insight into public sentiment in a geographic region. This insight can be enriched by visualising information in its geographic context. Two
underlying approaches for sentiment analysis are dictionarybased and machine learning. The former is popular for public sentiment analysis, and the latter has found limited use for aggregating public sentiment from Twitter data. The research presented in this paper aims to extend the machine learning approach for aggregating public sentiment. To this end, a framework for analysing and visualising public sentiment from a Twitter corpus is developed. A dictionary-based approach and a machine learning approach are implemented within the framework and compared using one UK case study, namely the royal birth of 2013. The case study validates the feasibility of the framework for analysis and rapid visualisation. One observation is that there is good correlation between the results produced by the popular dictionary-based approach and the machine learning approach when large volumes of tweets are analysed. However, for rapid analysis to be possible faster methods need to be developed using big data techniques and parallel methods.
Original languageEnglish
Title of host publication2013 IEEE International Conference on Big Data
PublisherIEEE Computer Society
Number of pages9
ISBN (Print)9781479912926
Publication statusPublished - 6 Oct 2013
Event2013 IEEE Conference on Big Data (IEEE BigData 2013) - Hyatt Regency Santa Clara, Santa Clara, United States
Duration: 6 Oct 20139 Oct 2013


Conference2013 IEEE Conference on Big Data (IEEE BigData 2013)
Country/TerritoryUnited States
CitySanta Clara


  • Sentiment analysis
  • Public opinion
  • Aggregate sentiment
  • Dictionary-based approach
  • Machine learning
  • Twitter
  • Royal birth


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