A Review of Two Text-Mining Packages

Davi, Angelique; Haughton, Dominique; Nasr, Nada; Shah, Gaurav; Skaletsky, Maria; Spack, Ruth

doi:10.1198/000313005x22987

Cited by 38 publications

(19 citation statements)

References 1 publication

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“…The preparation step included the spell-check of the text, the removal of hyphens, square brackets and braces etc.. Pre-processing enabled lemmatisation, which featured automatic correction of misspellings and the substitution of concepts with word forms that had identical roots. In addition, the pre-processing required a manual check of the frequency list to exclude concepts not relevant to the study (Davi et al, 2005). In the third step, the content analysis was applied to 154 papers to select a subset of features reflecting all-embracing dimensions of the discourse about the sharing economy.…”

Section: Methodsmentioning

confidence: 99%

Mapping the economics, social and technological attributes of the sharing economy

Davlembayeva

Papagiannidis

Alamanos

2019

ITP

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Purpose:The sharing economy is a socio-economic system in which individuals acquire and distribute goods and services among each other for free or for compensation through Internet platforms. The sharing economy has attracted the interest of the academic community, which examined the phenomenon from the economic, social and technological perspectives.Design: Given the lack of an overarching analysis of the sharing economy, this paper employs a quantitative content analysis approach to explore and synthesise relevant findings to facilitate the understanding of this emerging phenomenon. Findings:The paper identified and grouped findings under four themes, namely: collaborative consumption practices, resources, drivers of user engagement and impacts, each of which is discussed in relation to the three main themes, aiming to compare findings and then put forward an agenda for further research.Originality/value: The paper offers a balanced analysis of the building blocks of the sharing economy, to identify emerging themes within each stream, to discuss any contextual differences from a multi-stakeholder perspective and to propose directions for future studies.

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Section: Methodsmentioning

confidence: 99%

Mapping the economics, social and technological attributes of the sharing economy

Davlembayeva

Papagiannidis

Alamanos

2019

ITP

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“…Across large document collections, most text mining solutions are aimed at discovering patterns [12]. This offers the possibility of key words and elements extraction as well as the identification of relationships [16]. In addition, the use of text mining can be helpful for the analysis of massive amounts of existing text stream data [17].…”

Section: B Applications Of Text Miningmentioning

confidence: 99%

Benchmarking of Scientific Research Clusters by Use of Text Mining Algorithms on Textual Artefacts

Schroeder

Tummel

Isenhardt

et al. 2016

2016 International Conference on Information Systems Engineering (ICISE)

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The development of text mining algorithms is far reaching and thus, new application areas arise, inter alia in the framework of applying modern information technologies (e.g. big data analytics) in order to answer social and economic (research) questions (digital humanities). In this paper existing algorithms to reveal results in the context of benchmarking scientific research clusters are combined. For conducting the analysis, two data corpora are gathered. The first data corpus contains abstracts of publications according to the used application case of the Cluster of Excellence Tailor-Made Fuels from Biomass 'TMFB' at RWTH Aachen University. The second data corpus -based on the analysis of the first data corpus -contains content-related abstracts of research belonging to the relevant scientific community. In the framework of processing the gathered data, information extraction, information retrieval and named entity recognition among others are utilized for analyzing the publications in order to derive useful findings. Furthermore association mining findings are introduced. As an excerpt of the results, the identification of thematic alignments and developments over time are presented for both corpora and are subsequently compared.

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“…This means that those terms with the highest occurrence frequencies in a text are rated important. In spite of its simplicity this approach is widely used in text mining (Davi et al 2005) as it can be interpreted nicely and is computationally inexpensive.…”

Section: Count-based Evaluationmentioning

confidence: 99%

“…Commercial text mining products (Davi et al 2005) are typically built in monolithic structures regarding extensibility. This is inherent as their source code is normally not available.…”

Section: Data Structures and Algorithmsmentioning

confidence: 99%

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Text Mining Infrastructure inR

Feinerer¹,

Hornik²,

Meyer³

2008

J. Stat. Soft.

975

634

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During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.

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A Review of Two Text-Mining Packages

Cited by 38 publications

References 1 publication

Mapping the economics, social and technological attributes of the sharing economy

Mapping the economics, social and technological attributes of the sharing economy

Benchmarking of Scientific Research Clusters by Use of Text Mining Algorithms on Textual Artefacts

Text Mining Infrastructure inR

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