Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24030
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dc.contributor.authorOfek, Nir-
dc.contributor.authorPoria, Soujanya-
dc.contributor.authorRokach, Lior-
dc.contributor.authorCambria, Erik-
dc.contributor.authorHussain, Amir-
dc.contributor.authorShabtai, Asaf-
dc.date.accessioned2016-08-16T02:15:07Z-
dc.date.issued2016-06-
dc.identifier.urihttp://hdl.handle.net/1893/24030-
dc.description.abstractSentiment analysis in natural language text is a challenging task involving a deep understanding of both syntax and semantics. Leveraging the polarity of multiword expressions—or concepts—rather than single words can mitigate the difficulty of such a task as these expressions carry more contextual information than isolated words. Such contextual information is the key to understanding both the syntactic and semantic structure of natural language text and hence is useful in tasks such as sentiment analysis. In this work, we propose a new method to enrich SenticNet (a publicly available knowledge base for concept-level sentiment analysis) with domain-level concepts composed of aspects and sentiment word pairs, along with a measure of their polarity. We process a set of unlabeled texts and, by considering the statistical co-occurrence information, generate a direct acyclic graph (DAG) of concepts. The polarity score of known concepts is propagated and used to compute polarity scores of new concepts. By designing and implementing our exhaustive algorithm, we are able to use a seed set containing only two sentiment words (goodandbad). In our evaluation conducted on a dataset of hotel reviews, SenticNet was enriched by a factor of three (from 30,000 to nearly 90,000 concepts). The experiments demonstrate the merit of the concepts discovered by our method at improving sentence-level and aspect-level sentiment analysis tasks. Results of the two-factor ANOVA statistical test showed a confidence level of 95%, verifying that the improvements are statistically significant.en_UK
dc.language.isoen-
dc.publisherSpringer-
dc.relationOfek N, Poria S, Rokach L, Cambria E, Hussain A & Shabtai A (2016) Unsupervised Commonsense Knowledge Enrichment for Domain-Specific Sentiment Analysis, Cognitive Computation, 8 (3), pp. 467-477.-
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.-
dc.subjectSentiment analysisen_UK
dc.subjectSentiment lexiconen_UK
dc.subjectSenticNeten_UK
dc.subjectSentic patternsen_UK
dc.titleUnsupervised Commonsense Knowledge Enrichment for Domain-Specific Sentiment Analysisen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2999-12-31T00:00:00Z-
dc.rights.embargoreasonThe publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.-
dc.identifier.doihttp://dx.doi.org/10.1007/s12559-015-9375-3-
dc.citation.jtitleCognitive Computation-
dc.citation.issn1866-9956-
dc.citation.volume8-
dc.citation.issue3-
dc.citation.spage467-
dc.citation.epage477-
dc.citation.publicationstatusPublished-
dc.citation.peerreviewedRefereed-
dc.type.statusPublisher version (final published refereed version)-
dc.identifier.urlhttp://link.springer.com/article/10.1007/s12559-015-9375-3-
dc.author.emailahu@cs.stir.ac.uk-
dc.citation.date12/02/2016-
dc.contributor.affiliationBen-Gurion University of the Negev-
dc.contributor.affiliationUniversity of Stirling-
dc.contributor.affiliationBen-Gurion University of the Negev-
dc.contributor.affiliationNanyang Technological University-
dc.contributor.affiliationComputing Science - CSM Dept-
dc.contributor.affiliationBen-Gurion University of the Negev-
dc.rights.embargoterms2999-12-31-
dc.rights.embargoliftdate2999-12-31-
dc.identifier.isi000376284900007-
Appears in Collections:Computing Science and Mathematics Journal Articles

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