Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6295
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dc.contributor.authorSezerer, Erhan-
dc.contributor.authorTekir, Selma-
dc.date.accessioned2017-10-03T07:05:31Z-
dc.date.available2017-10-03T07:05:31Z-
dc.date.issued2017-
dc.identifier.citationSezerer, E., and Tekir, S. (2017). A relativistic opinion mining approach to detect factual or opinionated news sources. Lecture Notes in Computer Science, Volume 10440 LNCS, 303-312. doi:10.1007/978-3-319-64283-3_22en_US
dc.identifier.isbn9783319642826-
dc.identifier.issn0302-9743-
dc.identifier.issn1611-3349-
dc.identifier.urihttp://doi.org/10.1007/978-3-319-64283-3_22-
dc.identifier.urihttp://hdl.handle.net/11147/6295-
dc.description19th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2017; Lyon; France; 28 August 2017 through 31 August 2017en_US
dc.description.abstractThe credibility of news cannot be isolated from that of its source. Further, it is mainly associated with a news source’s trustworthiness and expertise. In an effort to measure the trustworthiness of a news source, the factor of “is factual or opinionated” must be considered among others. In this work, we propose an unsupervised probabilistic lexicon-based opinion mining approach to describe a news source as “being factual or opinionated”. We get words’ positive, negative, and objective scores from a sentiment lexicon and normalize these scores through the use of their cumulative distribution. The idea behind the use of such a statistical approach is inspired from the relativism that each word is evaluated with its difference from the average word. In order to test the effectiveness of the approach, three different news sources are chosen. They are editorials, New York Times articles, and Reuters articles, which differ in their characteristic of being opinionated. Thus, the experimental validation is done by the analysis of variance on these different groups of news. The results prove that our technique can distinguish the news articles from these groups with respect to “being factual or opinionated” in a statistically significant way.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey under contract number 114E784en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relationinfo:eu-repo/grantAgreement/TUBITAK/EEEAG/114E784en_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectData miningen_US
dc.subjectOpinion miningen_US
dc.subjectSentiment lexiconsen_US
dc.subjectNews articlesen_US
dc.subjectCumulative distributionen_US
dc.titleA relativistic opinion mining approach to detect factual or opinionated news sourcesen_US
dc.typeConference Objecten_US
dc.typeSubmitted Versionen_US
dc.authoridTR191338en_US
dc.authoridTR114496en_US
dc.departmentIzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.volumeVolume 10440 LNCSen_US
dc.identifier.startpage303en_US
dc.identifier.endpage312en_US
dc.identifier.scopusSCOPUS:2-s2.0-85028451727
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/978-3-319-64283-3_22-
dc.relation.doi10.1007/978-3-319-64283-3_22en_US
dc.coverage.doi10.1007/978-3-319-64283-3_22en_US
item.openairetypeConference Object-
item.openairetypeSubmitted Version-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.deptDepartment of Computer Engineering-
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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