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dc.contributor.advisorGulla, Jon Atlenb_NO
dc.contributor.advisorIngvaldsen, Jon Espennb_NO
dc.contributor.authorLægreid, Tarjeinb_NO
dc.contributor.authorSandal, Paul Christiannb_NO
dc.date.accessioned2014-12-19T13:34:17Z
dc.date.available2014-12-19T13:34:17Z
dc.date.created2010-09-05nb_NO
dc.date.issued2006nb_NO
dc.identifier349024nb_NO
dc.identifierntnudaim:1436nb_NO
dc.identifier.urihttp://hdl.handle.net/11250/251474
dc.description.abstractOnline financial news sources continuously publish information about actors involved in the Norwegian financial market. These are often short messages describing temporal relations. However, the amount of information is overwhelming and it requires a great effort to stay up to date on both the latest news and historical relations. Therefore it would have been advantageous to automatically analyse the information. In this report we present a framework for identifying actors and relations between them. Text mining techniques are employed to extract the relations and how they evolve over time. Techniques such as part of speech tagging, named entity identification, along with traditional information retrieval and information extraction methods are employed. Features extracted from the news articles are represented as vectors in a vector space. The framework employs the feature vectors to identify and describe relations between entities in the financial market. A qualitative evaluation of the framework shows that the approach has promising results. Our main finding is that vector representations of features have potential for detecting relations between actors, and how these relations evolve. We also found that the approach taken is dependent on an accurate identification of named entities.nb_NO
dc.languageengnb_NO
dc.publisherInstitutt for datateknikk og informasjonsvitenskapnb_NO
dc.subjectntnudaimno_NO
dc.subjectSIF2 datateknikkno_NO
dc.subjectProgram- og informasjonssystemerno_NO
dc.titleFinancial News Mining:: Extracting useful Information from Continuous Streams of Textnb_NO
dc.typeMaster thesisnb_NO
dc.source.pagenumber103nb_NO
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for datateknikk og informasjonsvitenskapnb_NO


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