Unsupervised fine-grained sentiment analysis system using lexicons and concepts

Nir Ofek, Lior Rokach

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations


Sentiment is mainly analyzed at a document, sentence or aspect level. Document or sentence levels could be too coarse since polar opinions can co-occur even within the same sentence. In aspect level sentiment analysis often opinion-bearing terms can convey polar sentiment in different contexts. Consider the following laptop review: “the big plus was a large screen but having a large battery made me change my mind,” where polar opinions co-occur in the same sentence, and the opinion term that describes the opinion targets (“large”) encodes polar sentiments: a positive for screen, and a negative for battery. To parse these differences, our approach is to identify opinions with respect to the specific opinion targets, while taking the context into account. Moreover, considering that there is a problem of obtaining an annotated training set in each context, our approach uses unlabeled data.

Original languageEnglish
Title of host publicationSemantic Web Evaluation Challenge - SemWebEval 2014 at ESWC 2014, Revised Selected Papers
EditorsTommaso Di Noia, Valentina Presutti, Diego Reforgiato Recupero, Iván Cantador, Christoph Lange, Christoph Lange, Anna Tordai, Christoph Lange, Milan Stankovic, Erik Cambria, Angelo Di Iorio
PublisherSpringer Verlag
Number of pages6
ISBN (Electronic)9783319120232
StatePublished - 1 Jan 2014

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


  • Fine-grained sentiment analysis
  • Lexicon
  • Opinion mining

ASJC Scopus subject areas

  • Computer Science (all)
  • Mathematics (all)


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