What can Venn diagrams teach us about doing data science better?

Ho, Sung Yang; Tan, Sophia; Sze, Chun Chau; Wong, Limsoon; Goh, Wilson Wen Bin

doi:10.1007/s41060-020-00230-4

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…4 is another visualisation of the TOP100 terms for each measure. It is a Venn diagram representation (Ho et al, 2021), the intersections of circles give the list of common terms between measures. The intersection between H-TFIDF and the most used terms in UK is greater than TF-IDF and the most used terms.…”

Section: Discussionmentioning

confidence: 99%

“…This corpus has been also used by other studies. (Ferrara, 2020) characterizes the impact of Twitter bots on COVID-19 conspiracies and (Jiang et al, 2020) analyses the political polarization in the United States. This corpus have been enhanced as well by (Lopez and Gallemore, 2020) to complete their collection with other keywords and apply state-of-the-art methods on sentiment analysis and named entity recognition.…”

Section: Description Of the Corpusmentioning

confidence: 99%

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H-TFIDF: What makes areas specific over time in the massive flow of tweets related to the covid pandemic?

Decoupes

Kafando

Roche

et al. 2021

AGILE GIScience Ser.

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Abstract. Data produced by social networks may contain weak signals of possible epidemic outbreaks. In this paper, we focus on Twitter data during the waiting period before the appearance of COVID-19 first cases outside China. Among the huge flow of tweets that reflects a global growing concern in all countries, we propose to analyze such data with an adaptation of the TF-IDF measure. It allows the users to extract the discriminant vocabularies used across time and space. The results are then discussed to show how the specific spatio-temporal anchoring of the extracted terms make it possible to follow the crisis dynamics on different scales of time and space.

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Section: Discussionmentioning

confidence: 99%

Section: Description Of the Corpusmentioning

confidence: 99%

H-TFIDF: What makes areas specific over time in the massive flow of tweets related to the covid pandemic?

Decoupes

Kafando

Roche

et al. 2021

AGILE GIScience Ser.

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show abstract

“…The LEAP4FNSSA lexicon obtained is integrated into the KEOPS (Knowledge ExtractOr Pipeline System) tool that uses text mining approaches to highlight knowledge from heterogenous textual data [5] . KEOPS is currently implemented on LEAP4FNSSA data in order to extract, visualise and analyse food security themes with maps, graphs, curves, and Venn diagrams [8] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

LEAP4FNSSA lexicon: Towards a new dataset of keywords dealing with food security

et al. 2022

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“…For the data representation, we take advantages of Venn Diagram [ 16 ], see in Appendix Fig. 10 the distribution of the Papers2-title corpus terms.…”

Section: Methodsmentioning

confidence: 99%

ITEXT-BIO: Intelligent Term EXTraction for BIOmedical analysis

Kafando

Decoupes

Valentin

et al. 2021

Health Inf Sci Syst

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Here, we introduce ITEXT-BIO, an intelligent process for biomedical domain terminology extraction from textual documents and subsequent analysis. The proposed methodology consists of two complementary approaches, including free and driven term extraction. The first is based on term extraction with statistical measures, while the second considers morphosyntactic variation rules to extract term variants from the corpus. The combination of two term extraction and analysis strategies is the keystone of ITEXT-BIO. These include combined intra-corpus strategies that enable term extraction and analysis either from a single corpus (intra), or from corpora (inter). We assessed the two approaches, the corpus or corpora to be analysed and the type of statistical measures used. Our experimental findings revealed that the proposed methodology could be used: (1) to efficiently extract representative, discriminant and new terms from a given corpus or corpora, and (2) to provide quantitative and qualitative analyses on these terms regarding the study domain.

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What can Venn diagrams teach us about doing data science better?

Cited by 7 publications

References 25 publications

H-TFIDF: What makes areas specific over time in the massive flow of tweets related to the covid pandemic?

H-TFIDF: What makes areas specific over time in the massive flow of tweets related to the covid pandemic?

LEAP4FNSSA lexicon: Towards a new dataset of keywords dealing with food security

ITEXT-BIO: Intelligent Term EXTraction for BIOmedical analysis

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