Difference between revisions of "SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining"
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The motivation behind this research is to aid [[AddressesProblem:: Opinion mining]] by providing an off the shelf lexical resource that provides a granular level of opinion tags for a large set of words. | The motivation behind this research is to aid [[AddressesProblem:: Opinion mining]] by providing an off the shelf lexical resource that provides a granular level of opinion tags for a large set of words. | ||
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| + | The development method of SentiWordNet is an adaptation of PN-polarity [Esuli and Sebastiani, 2005] and SO-polarity [Esuli and Sebastiani, 2006] identification methods. The proposed method uses a set of ternary classifiers, capable of deciding whether a synset is Positive, Negative or Objective. Each ternary classifier differs from other in two perspectives, first the training data used, secondly, the learner. Thus each ternary classifier produces different classification results for a synset. The final opinion score is calculated using the normalization of scores from all the classifiers. | ||
== Background == | == Background == | ||
Revision as of 19:48, 26 September 2012
Citation
Andrea Esuli , Fabrizio Sebastiani, "SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining". In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC’06),417-422.
Online version
LREC 2006, SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining
Summary
This paper discusses the development of SentiWordNet, a lexical resource in which each WordNet synset s is associated to three numerical scores Obj(s), Pos(s), Neg(s) used to describe how objective, positive and negative the terms contained in the synset are.
The motivation behind this research is to aid Opinion mining by providing an off the shelf lexical resource that provides a granular level of opinion tags for a large set of words.
The development method of SentiWordNet is an adaptation of PN-polarity [Esuli and Sebastiani, 2005] and SO-polarity [Esuli and Sebastiani, 2006] identification methods. The proposed method uses a set of ternary classifiers, capable of deciding whether a synset is Positive, Negative or Objective. Each ternary classifier differs from other in two perspectives, first the training data used, secondly, the learner. Thus each ternary classifier produces different classification results for a synset. The final opinion score is calculated using the normalization of scores from all the classifiers.
Background
In Opinion mining there are mainly three tasks related to tagging the given text with expressed opinion:
- Determining text SO-polarity by checking whether the text has factual nature or expresses opinion.Pang and Lee,2004 Hatzivassiloglou,2003
- Determining text PN-polarity by checking whether text expresses a positive or negative opinion on subject matter.Pang and Lee,2004 Turney,_ACL_2002
- Determining the strength of text PN-polarity by checking the expressed opinion's emphasis (Weak, Mild, Strong).Pang and Lee,2005
Visualization
The given synset's scores can be visualized in a self descriptive triangle. 
