OpinoFetch: a practical and efficient approach to collecting opinions on arbitrary entities

Kavita Ganesan, Cheng Xiang Zhai

Research output: Contribution to journalArticlepeer-review


The abundance of opinions on the Web is now becoming a critical source of information in a variety of application areas such as business intelligence, market research and online shopping. Unfortunately, due to the rapid growth of online content, there is no one source to obtain a comprehensive set of opinions about a specific entity or a topic, making access to such content severely limited. While previous works have been focused on mining and summarizing online opinions, there is limited work on exploring the automatic collection of opinion content on the Web. In this paper, we propose a lightweight and practical approach to collecting opinion containing pages, namely review pages on the Web for arbitrary entities. We leverage existing Web search engines and use a novel information network called the FetchGraph to efficiently obtain review pages for entities of interest. Our experiments in three different domains show that our method is more effective than plain search engine results and we are able to collect entity specific review pages efficiently with reasonable precision and accuracy.

Original languageEnglish (US)
Pages (from-to)530-558
Number of pages29
JournalInformation Retrieval
Issue number6
StatePublished - Dec 1 2015


  • Opinion aggregation
  • Opinion analysis
  • Opinion collection
  • Opinion crawling
  • Review aggregation
  • Review crawling

ASJC Scopus subject areas

  • Information Systems
  • Library and Information Sciences

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