Detecting trends on the Web: A multidisciplinary approach
Author
dc.contributor.author
Dueñas Fernández, Rodrigo
Author
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Velásquez Silva, Juan
es_CL
Author
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L’Huillier, Gastón
es_CL
Admission date
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2014-12-19T03:48:52Z
Available date
dc.date.available
2014-12-19T03:48:52Z
Publication date
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2014
Cita de ítem
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Information Fusion 20 (2014) 129–135
en_US
Identifier
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DOI: 10.1016/j.inffus.2014.01.006
Identifier
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https://repositorio.uchile.cl/handle/2250/126705
General note
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Artículo de publicación ISI
en_US
Abstract
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This paper introduces a framework for trend modeling and detection on the Web through the usage of
Opinion Mining and Topic Modeling tools based on the fusion of freely available information. This framework
consists of a four step model that runs periodically: crawl a set of predefined sources of documents;
search for potential sources and extract topics from the retrieved documents; retrieve opinionated documents
from social networks for each detected topic and extract sentiment information from them. The
proposed framework was applied to a set of 20 sources of documents over a period of 8 months. After the
analysis period and that the proposed experiments were run, an F-Measure of 0.56 was obtained for the
detection of significant events, implying that the proposed framework is a feasible model of how trends
could be represented through the analysis of documents freely available on the Web.
en_US
Patrocinador
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This work was partially supported by FONDEF project
D10I-1198, entitled WHALE: Web Hypermedia Analysis Latent
Environment and the Millennium Institute on Complex Engineering
Systems (ICM: P-05-004-F, CONICYT: FBO16).