Mining on Terms Extraction from Web News

Hsu, L.

doi:10.1007/978-3-642-16693-8_21

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Cited by 3 publications

(3 citation statements)

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“…In [6], the authors describe the use of data mining techniques to analyze web news. It is concluded from that study that web mews mining at the terms level serves as a powerful technique to manage knowledge encapsulated in large web news collection.…”

Section: Background and Related Workmentioning

confidence: 99%

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Web news mining in an evolving framework

et al. 2016

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Section: Background and Related Workmentioning

confidence: 99%

“…During the last years, there have been many approaches related with classification, clustering, categorization and summarization of news articles [3,4,5,6,7,8]. If we consider those approaches which classify news into predefined categories, all of them use a statistic classifier over time.…”

Section: Introductionmentioning

confidence: 99%

Web news mining in an evolving framework

et al. 2016

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“…Association rules have also been used to analyse news disseminated on the web: Hsu [12] has proposed the Web News Search System to discover 'useful' news, and Kittiphattanabawon and Theeramunkong [13] mined relations between Thai news articles concerning politics, economics and crime. A corollary of this concerns mining text snippets, e.g.…”

Section: Related Workmentioning

confidence: 99%

visualRSS: A Platform to Mine and Visualise Social Data from RSS Feeds

O'Shea

Levene

2012

Current Trends in Web Engineering

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Abstract. RSS, a popular method of syndicating frequently updated on-line content, allows data to be stored in a semi-structured, XMLbased format. Much work has been carried out applying data mining techniques to RSS, but in this paper we propose the visualRSS (vRSS) application as a platform to mine and visualise data trends in RSS feeds, by tracking changes in keyword frequencies as a source of social data. Core components of vRSS's architecture to manipulate RSS feeds are described. We also present the results of vRSS's initial experimental usage involving 36 students in late 2011, concerning our research into preferences of mining types and visualisations.

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