Application of text mining in the biomedical domain

Fleuren, Wilco W. M.; Alkema, Wynand

doi:10.1016/j.ymeth.2015.01.015

Cited by 163 publications

(112 citation statements)

References 122 publications

Supporting

Mentioning

107

Contrasting

Order By: Relevance

“…By means of NER, keywords that are found in the text need to be linked to the specific concept that is being referred to in the document. Thus, concepts can be defined as a biological entity that can be referred to by multiple keywords [26].…”

Section: A Methodological Bridging the Gap: Scientific Output And Tementioning

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)

View full text Add to dashboard Cite

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.

show abstract

Section: A Methodological Bridging the Gap: Scientific Output And Tementioning

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)

View full text Add to dashboard Cite

show abstract

“…Text Mining is the branch of Data Mining concerning the process of deriving high-quality information from text; see also [3].This area underwent noteworthy improvements in recent years (see, e.g., [4,5]), 2 Mathematical Problems in Engineering with a number of concurrent factors contributing to its progress, first of all the continuous expansion of the Internet and the demand for effective search strategies. However, the above described problem turns out to be a very difficult case of the classification problem, for several reasons.…”

Section: Introductionmentioning

confidence: 99%

Identifying e-Commerce in Enterprises by means of Text Mining and Classification Algorithms

Bianchi

Bruni

Scalfati

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Monitoring specific features of the enterprises, for example, the adoption of e-commerce, is an important and basic task for several economic activities. This type of information is usually obtained by means of surveys, which are costly due to the amount of personnel involved in the task. An automatic detection of this information would allow consistent savings. This can actually be performed by relying on computer engineering, since in general this information is publicly available on-line through the corporate websites. This work describes how to convert the detection of e-commerce into a supervised classification problem, where each record is obtained from the automatic analysis of one corporate website, and the class is the presence or the absence of e-commerce facilities. The automatic generation of similar data records requires the use of several Text Mining phases; in particular we compare six strategies based on the selection of best words and best n-grams. After this, we classify the obtained dataset by means of four classification algorithms: Support Vector Machines; Random Forest; Statistical and Logical Analysis of Data; Logistic Classifier. This turns out to be a difficult case of classification problem. However, after a careful design and set-up of the whole procedure, the results on a practical case of Italian enterprises are encouraging.

show abstract

“…Given the conspicuous absence of education policy studies using big data, I primarily draw upon the literature on big data in public policy and computational social science-the emerging field at the convergence of computer science and the social sciences, using computational modeling to analyze massive amounts of digital data harvested mostly from digital media sources to study social phenomenon (Lazer et al, 2009;Shah, Cappella, & Neuman, 2015;Watts, 2013). Grounded in the broad literature relevant to education policy research, I then introduce three methodological frontiers of mining massive amounts of text data (i.e., a corpus of texts; Fleuren & Alkema, 2015;Hearst, 1999): topic models, network text analysis, and sentiment analysis. In particular, I examine the assumptions, key concepts, merits, and caveats of each of the three analytical approaches.…”

Section: Introductionmentioning

confidence: 99%

Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges

Wang

2017

EPAA

View full text Add to dashboard Cite

Abstract:Despite abundant data and increasing data availability brought by technological advances, there has been very limited education policy studies that have capitalized on big data-characterized by large volume, wide variety, and high velocity. Drawing on the recent progress of using big data in public policy and computational social science research, this commentary discusses how to approach big data and how big data can be used in education policy research. First, I introduce big data that is potentially relevant to education policy research. I then present methodological frontiers by examining the assumptions, key concepts, merits, and caveats of three commonly used analytical approaches to mining massive amounts of text data: topic models, network text analysis, and sentiment analysis. Next, to ensure the veracity of using big data in education policy research, I debunk three methodological misconceptions. This commentary concludes with a discussion on developing interdisciplinary research capacity and addressing the privacy concerns and ethical conundrums as we explore a research agenda of using big data in education policy. Keywords: big data; education policy; network text analysis; sentiment analysis; text mining; topic models Investigación de política educativa en la era de los grandes datos: Fronteras metodológicas, equívocos, y desafíos Resumen: A pesar de la abundancia de datos y del aumento de la disponibilidad de datos traídos por los avances tecnológicos, hubo estudios de políticas educativas muy limitadas que usaron datos importantes, caracterizados por gran volumen, gran variedad y alta velocidad. En base al reciente progreso del uso de grandes datos en investigaciones de políticas públicas y de ciencia social computacional, este trabajo pretende demostrar el potencial de datos importantes y la gran cantidad de datos que pueden ser utilizados en la investigación de políticas educativas. En primer lugar, introduzca datos importantes que son potencialmente relevantes para la investigación de políticas educativas. Puedo, entonces, presentar fronteras metodológicas, examinando los supuestos conceptos clave, méritos y salvedades de tres enfoques analíticos comúnmente usados en la minería de grandes cantidades de datos de texto: modelos de tópicos, análisis de conexiones textuales y análisis de sentimientos. A continuación, para garantizar la veracidad del uso de grandes datos en la investigación sobre políticas educativas, desenmascaramos tres equívocos metodológicos. Este artículo concluye con una discusión sobre el desarrollo de la capacidad de investigación interdisciplinaria abordando las preocupaciones de privacidad y los enigmas éticos a medida que exploramos una agenda de investigación de uso de datos importantes en la política educativa. Palabras-clave: datos grandes; política educativa; análisis de conexiones textuales; análisis de sentimientos; minería de textos; modelos de temas Pesquisa de política educacional na era dos grandes dados: Fronteiras metodológicas, equívocos, e desafios ...

show abstract

Application of text mining in the biomedical domain

Cited by 163 publications

References 122 publications

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

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

Identifying e-Commerce in Enterprises by means of Text Mining and Classification Algorithms

Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges

Contact Info

Product

Resources

About