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Distinguishing the Popularity between Topics: A System for Up-to-Date Opinion Retrieval and Mining in the Web

  • Nikolaos Pappas
  • Georgios Katsimpras
  • Efstathios Stamatatos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7817)

Abstract

The constantly increasing amount of opinionated texts found in the Web had a significant impact in the development of sentiment analysis. So far, the majority of the comparative studies in this field focus on analyzing fixed (offline) collections from certain domains, genres, or topics. In this paper, we present an online system for opinion mining and retrieval that is able to discover up-to-date web pages on given topics using focused crawling agents, extract opinionated textual parts from web pages, and estimate their polarity using opinion mining agents. The evaluation of the system on real-world case studies, demonstrates that is appropriate for opinion comparison between topics, since it provides useful indications on the popularity based on a relatively small amount of web pages. Moreover, it can produce genre-aware results of opinion retrieval, a valuable option for decision-makers.

Keywords

Opinion Retrieval Text Mining Sentiment Analysis Information Extraction Utility-Based Agents 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nikolaos Pappas
    • 1
    • 2
  • Georgios Katsimpras
    • 2
  • Efstathios Stamatatos
    • 2
  1. 1.Idiap Research InstituteMartignySwitzerland
  2. 2.Dep. of Information and Communication Systems EngineeringUniversity of the AegeanSamosGreece

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