A fuzzy logic approach for opinion mining on large scale twitter data

Li Bing, Chun Chung Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

31 Citations (Scopus)


Recently, some efforts have been made to mine social media for the analysis of public sentiment. By means of a literature review on early works related to social media analytics especially on opinion mining, it was recognized that in the real life social media environment, the structure of the data is commonly not clear and it does not directly generate enough information to fully represent any selected target. However, most of these works were unable to accurately extract clear indications of general public opinion from the ambiguous social media data. They also lacked the capacity to summarize multi-characteristics from the scattered mass of social data and use it to compile useful models, also lacked any efficient mechanism for managing the big data. Motivated by these research problems, this paper proposes a novel matrix-based fuzzy algorithm, called the FMM system, to mine the defined multi-layered Twitter data. Through sets of comparable experiments applied on Twitter data, the proposed FMM system achieved an excellent performance, with both fast processing speeds and high predictive accuracy.
Original languageEnglish
Title of host publicationProceedings - 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, UCC 2014
Number of pages6
ISBN (Electronic)9781479978816
Publication statusPublished - 29 Jan 2014
Event7th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2014 - London, United Kingdom
Duration: 8 Dec 201411 Dec 2014


Conference7th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2014
Country/TerritoryUnited Kingdom


  • Big data
  • Data mining
  • Social media analytics

ASJC Scopus subject areas

  • Software

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