Reprint of: Computational approaches for mining user's opinions on the Web 2.0

Gerald Petz, Michał Karpowicz, Harald Fürschuß, Andreas Auinger, Václav Stříteský, Andreas Holzinger

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


The emerging research area of opinion mining deals with computational methods in order to find, extract and systematically analyze people's opinions, attitudes and emotions towards certain topics. While providing interesting market research information, the user generated content existing on the Web 2.0 presents numerous challenges regarding systematic analysis, the differences and unique characteristics of the various social media channels being one of them. This article reports on the determination of such particularities, and deduces their impact on text preprocessing and opinion mining algorithms. The effectiveness of different algorithms is evaluated in order to determine their applicability to the various social media channels. Our research shows that text preprocessing algorithms are mandatory for mining opinions on the Web 2.0 and that part of these algorithms are sensitive to errors and mistakes contained in the user generated content.

Original languageEnglish
Pages (from-to)510-519
Number of pages10
JournalInformation Processing and Management
Issue number4
Publication statusPublished - 28 Jul 2015


  • Data mining
  • Noisy text
  • Opinion mining
  • Text preprocessing
  • User generated content


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