Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6295
Title: A relativistic opinion mining approach to detect factual or opinionated news sources
Authors: Sezerer, Erhan
Tekir, Selma
Sezerer, Erhan
Tekir, Selma
Izmir Institute of Technology. Computer Engineering
Keywords: Data mining
Opinion mining
Sentiment lexicons
News articles
Cumulative distribution
Issue Date: 2017
Publisher: Springer Verlag
Source: Sezerer, 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_22
Abstract: The 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.
Description: 19th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2017; Lyon; France; 28 August 2017 through 31 August 2017
URI: http://doi.org/10.1007/978-3-319-64283-3_22
http://hdl.handle.net/11147/6295
ISBN: 9783319642826
ISSN: 0302-9743
1611-3349
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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