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DETECTION OF EMERGING DISRUPTIVE FIELDS USING ABSTRACTS OF SCIENTIFIC ARTICLES
Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

With the significant advancementstaking place in the last three decades in the field ofInformation Technology (IT), we are witnesses of an era unprecedented to the standards that mankind was used to, for centuries. Having access to a huge amount of dataalmost instantly,entails certainadvantages. One of which is the ability to observe in which segments of their expertise do scientists focus their research. That kind of knowledge, if properly appraised could hold the key to explaining what the new directions of the applied sciences will be and thus could help to constructing a “map” of the future developments from the Research and Development labs of the industries worldwide.Though the above statement may be considered too “futuristic”, already there have been documented attempts in the literature that have been fruitful into using vast amount of scientific data in an attempt to outline future scientific trends and thus scientific discoveries.The purpose of this research is to try to use a pioneeringmethodof modeling text corpora that already hasbeen used previously to the task of mapping the history of scientific discovery, that of Latent Dirichlet Allocation (LDA)and try to evaluate itsusability into detecting emerging research trends by the mere use of only the “Abstracts” from a collectionof scientific articles.To do that an experimental set is being utilized and the process is repeated over three experimental runs.The results, although not the ones that would validate the hypothesis, are showing that with certain improvements in the processing the hypothesis could be confirmed.

Place, publisher, year, edition, pages
2017. , p. 51
Keyword [en]
Technological Forecasting, Text Mining, Text Classification, Topic Detection, Research Evolution
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hj:diva-37904ISRN: JU-JTH-IKA-2-20170060OAI: oai:DiVA.org:hj-37904DiVA, id: diva2:1159543
Subject / course
JTH, Informatics
Presentation
2017-09-26, E2433, School of Engineering, Jönköping University, Jönköping, 22:46 (English)
Supervisors
Examiners
Available from: 2017-12-05 Created: 2017-11-22 Last updated: 2017-12-05Bibliographically approved

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