Improving Citation Sentiment and Purpose Classification Using Hybrid Deep Neural Network Model

Yousif, Abdallah; Niu, Zhendong; Nyamawe, Ally S.; Hu, Yating

doi:10.1007/978-3-319-99010-1_30

Cited by 5 publications

(1 citation statement)

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“…Kayal et al [220] developed a method to automatically extract funding information from scientific articles. Yousif et al [221] developed a model based on deep learning to extract the purpose of a citation to an article. Coupling this information with the number of citation and the applications of the articles results gives good measure to evaluate the efficiency of the funding.…”

Section: Published Articlesmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

203

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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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Section: Published Articlesmentioning

confidence: 99%