Web news mining in an evolving framework

Iglesias, José Antonio; Tiemblo, Alexandra; Ledezma, Agapito; Sanchís, Araceli

doi:10.1016/j.inffus.2015.07.004

Cited by 39 publications

(13 citation statements)

References 34 publications

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“…and Australia (6.6%) and these results were highly similar with the incidence cases as stated by the WHO [25]. This MIMO algorithm with a fuzzy classifier was first employed by Iglesias et al [27] to categorize 6 major sectorshealthcare, technology, sports, commerce, and cultural art. Web mining techniques have been utilized for epidemic vigilance in the past as well [28].…”

Section: The Famec Model For Tracking Covid-19supporting

confidence: 70%

Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks

Qazi

Ahmad

Raza

2020

Studies in Computational Intelligence

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The novel coronavirus disease (COVID-19) causes serious respiratory tract infections in humans, and worse leads to mortality in old-aged people or individuals with co-morbidities. Websites and online social platforms generate a gargantuan amount of data in myriad aspects namely-technology, global news, human healthcare, medicine, socio-political domain, etc., aiding to decipher significant knowledge using web mining. Since the outbreak, people from different geographical locations used hashtags about novel coronavirus. The FAMEC model, the Honghou Hybrid System (HHS), the COVID Tracking Project of Twitter are a few examples of computational intelligent online social trackers that have been devised to track the COVID-19 pandemic. Researchers have identified the significance of tweets to be consistent with the CDC and the WHO reports and discerned that mining of such personal tweets was effective to track, manage, and predict the mortality and morbidity rates, identify the geographic location of patients infected which would, in turn, lead to rapid treatment assessment, employment of telemedicine and sanitization of such regions. This chapter presents how computational intelligence along with online social networks can be used for tracking COVID-19 patients. IntroductionThe novel coronavirus, also called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic infectious disease known as coronavirus disease 2019 (COVID-19) which was initially observed in Wuhan city, China, in December 2019. This pandemic has led to a drastic change in the lifestyle around the world, with many people in quarantine, gigantic change in healthcare infrastructure,

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Section: The Famec Model For Tracking Covid-19supporting

confidence: 70%

Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks

Qazi

Ahmad

Raza

2020

Studies in Computational Intelligence

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“…Two recent studies, conducted by Iglesias et al [152] and Za'in et al [153], proposed a different approach based on evolving fuzzy systems (EFS). It allows the updating of the structure and parameters of an evolving classifier, aids in coping with huge volumes of web news, and enables the processing of data online and in real time, which is essential in real-time web news articles.…”

Section: Web Miningmentioning

confidence: 99%

“…It allows the updating of the structure and parameters of an evolving classifier, aids in coping with huge volumes of web news, and enables the processing of data online and in real time, which is essential in real-time web news articles. Iglesias et al [152] developed a web news mining based on eClass0 classifier, while Za'in et al [153] proposed a web news mining framework built on fuzzy evolving type-2 classifier (eT2Class), which outperforms other consolidated algorithms.…”

Section: Web Miningmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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“…It is also used to manage the web news article and also for user recommendation tasks. An approach based on Evolving Fuzzy System (EFS) [7] was used for web news mining. It classified different web news articles into various topics areas based on the text content of the articles.…”

Section: Introductionmentioning

confidence: 99%

Categorizing online news articles using penguin search optimization algorithm

Nithya

Sivakumari

2018

IJET

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Online news is an emerging channel where the internet users can get news. Analyzing huge volume of online news articles is a challenging one, because online news articles are generated and updated time to time. Big data techniques are used to tackle this problem. In order to classify the news articles into different categories, an approach based on Evolving Fuzzy Systems(EFS) was used. It categories news articles based on the changes in the content of the corresponding articles. However, it has the problem in the selection of threshold value. Moreover Gaussian membership function is used in EFS that describes the closeness to the prototype. Sometimes it is ha rd to justify. So in this paper, a Penguins Search Optimization Algorithm(PeSOA) is introduced to optimize the pruning threshold value and a bell shaped fuzzy membership function is introduced to define the closeness to the prototype. The optimized pruning threshold is used in term filtering which prune the generated terms based on their frequencies of occurrence throughout the collection. Then the fuzzy rules are generated by EFS where bell shaped fuzzy membership function is used to define the closeness to the prototype. Based on t he fuzzy rules the online news articles are categorized.

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Web news mining in an evolving framework

Cited by 39 publications

References 34 publications

Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks

Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks

Text Mining in Big Data Analytics

Categorizing online news articles using penguin search optimization algorithm

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