Semantic Mapping

Towards Contextual and Trend Analysis of Behaviours and Practices

Research output: Contribution to journalConference article

5 Citations (Scopus)

Abstract

As a platform for unsupervised data mining and pattern recognition, we use Correspondence Analysis on Twitter content from May to December 2015. The following data characteristics are well addressed: exponentially distributed data properties, and major imbalance between categories. Contextualization is supported. To both focus on informative resolution scale in one's data, and to handle large data sets, the granularity of point clouds offers benefits.

Original languageEnglish
Pages (from-to)1207-1225
Number of pages19
JournalCEUR Workshop Proceedings
Volume1609
Publication statusPublished - 25 Jul 2016
Externally publishedYes
Event7th International Conference of the CLEF Association: Experimental IR Meets Multilinguality, Multimodality, and Interaction - University of Évora, Evora, Portugal
Duration: 5 Sep 20168 Sep 2016
Conference number: 7
http://clef2016.clef-initiative.eu/ (Link to Event Website)

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Pattern recognition
Data mining
Semantics

Cite this

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title = "Semantic Mapping: Towards Contextual and Trend Analysis of Behaviours and Practices",
abstract = "As a platform for unsupervised data mining and pattern recognition, we use Correspondence Analysis on Twitter content from May to December 2015. The following data characteristics are well addressed: exponentially distributed data properties, and major imbalance between categories. Contextualization is supported. To both focus on informative resolution scale in one's data, and to handle large data sets, the granularity of point clouds offers benefits.",
author = "Fionn Murtagh",
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Semantic Mapping : Towards Contextual and Trend Analysis of Behaviours and Practices . / Murtagh, Fionn.

In: CEUR Workshop Proceedings, Vol. 1609, 25.07.2016, p. 1207-1225.

Research output: Contribution to journalConference article

TY - JOUR

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AU - Murtagh, Fionn

PY - 2016/7/25

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AB - As a platform for unsupervised data mining and pattern recognition, we use Correspondence Analysis on Twitter content from May to December 2015. The following data characteristics are well addressed: exponentially distributed data properties, and major imbalance between categories. Contextualization is supported. To both focus on informative resolution scale in one's data, and to handle large data sets, the granularity of point clouds offers benefits.

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M3 - Conference article

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JO - CEUR Workshop Proceedings

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