Extraction of temporal networks from term co-occurrences in online textual sources.
A stream of unstructured news can be a valuable source of hidden relations between different entities, such as financial institutions, countries, or persons. We present an approach to continuously collect online news, recognize relevant entities in them, and extract time-varying networks. The nodes...
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doaj-5c0f14ddff454c2a8a65d37637e006092020-11-25T01:20:37ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01912e9951510.1371/journal.pone.0099515Extraction of temporal networks from term co-occurrences in online textual sources.Marko PopovićHrvoje ŠtefančićBorut SlubanPetra Kralj NovakMiha GrčarIgor MozetičMichelangelo PuligaVinko ZlatićA stream of unstructured news can be a valuable source of hidden relations between different entities, such as financial institutions, countries, or persons. We present an approach to continuously collect online news, recognize relevant entities in them, and extract time-varying networks. The nodes of the network are the entities, and the links are their co-occurrences. We present a method to estimate the significance of co-occurrences, and a benchmark model against which their robustness is evaluated. The approach is applied to a large set of financial news, collected over a period of two years. The entities we consider are 50 countries which issue sovereign bonds, and which are insured by Credit Default Swaps (CDS) in turn. We compare the country co-occurrence networks to the CDS networks constructed from the correlations between the CDS. The results show relatively small, but significant overlap between the networks extracted from the news and those from the CDS correlations.http://europepmc.org/articles/PMC4254290?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Marko Popović Hrvoje Štefančić Borut Sluban Petra Kralj Novak Miha Grčar Igor Mozetič Michelangelo Puliga Vinko Zlatić |
spellingShingle |
Marko Popović Hrvoje Štefančić Borut Sluban Petra Kralj Novak Miha Grčar Igor Mozetič Michelangelo Puliga Vinko Zlatić Extraction of temporal networks from term co-occurrences in online textual sources. PLoS ONE |
author_facet |
Marko Popović Hrvoje Štefančić Borut Sluban Petra Kralj Novak Miha Grčar Igor Mozetič Michelangelo Puliga Vinko Zlatić |
author_sort |
Marko Popović |
title |
Extraction of temporal networks from term co-occurrences in online textual sources. |
title_short |
Extraction of temporal networks from term co-occurrences in online textual sources. |
title_full |
Extraction of temporal networks from term co-occurrences in online textual sources. |
title_fullStr |
Extraction of temporal networks from term co-occurrences in online textual sources. |
title_full_unstemmed |
Extraction of temporal networks from term co-occurrences in online textual sources. |
title_sort |
extraction of temporal networks from term co-occurrences in online textual sources. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2014-01-01 |
description |
A stream of unstructured news can be a valuable source of hidden relations between different entities, such as financial institutions, countries, or persons. We present an approach to continuously collect online news, recognize relevant entities in them, and extract time-varying networks. The nodes of the network are the entities, and the links are their co-occurrences. We present a method to estimate the significance of co-occurrences, and a benchmark model against which their robustness is evaluated. The approach is applied to a large set of financial news, collected over a period of two years. The entities we consider are 50 countries which issue sovereign bonds, and which are insured by Credit Default Swaps (CDS) in turn. We compare the country co-occurrence networks to the CDS networks constructed from the correlations between the CDS. The results show relatively small, but significant overlap between the networks extracted from the news and those from the CDS correlations. |
url |
http://europepmc.org/articles/PMC4254290?pdf=render |
work_keys_str_mv |
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