Skip to main navigation Skip to search Skip to main content

Determining the polarity of postings for discussion search

  • Ingo Frommholz
  • , Marc Lechtenfeld

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

When performing discussion search it might be desirable to consider non-topical measures like the number of positive and negative replies to a posting, for instance as one possible indicator for the trustworthiness of a comment. Systems like POLAR are able to integrate such values into the retrieval function. To automatically detect the polarity of postings, they need to be classified into positive and negative ones w.r.t.\ the comment or document they are annotating. We present a machine learning approach for polarity detection which is based on Support Vector Machines. We discuss and identify appropriate term and context features. Experiments with ZDNet News show that an accuracy of around 79\%-80\% can be achieved for automatically classifying comments according to their polarity.
Original languageEnglish
Title of host publicationnan
PublisherGesellschaft für Informatik e.V.
Publication statusPublished - 1 Jan 2008
EventInformation Retrieval Workshop at LWA 2008 -
Duration: 1 Jan 2008 → …

Conference

ConferenceInformation Retrieval Workshop at LWA 2008
Period1/01/08 → …
OtherInformation Retrieval Workshop at LWA 2008

Keywords

  • search engine

Fingerprint

Dive into the research topics of 'Determining the polarity of postings for discussion search'. Together they form a unique fingerprint.

Cite this