[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119304-en":3,"doc-seo-119304-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119304,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","JRC Terrorism and Extremism Database - Data Quality Optimisation for Machine Learning","A JRC technical report provides evidence-based scientific support for European policymaking by improving the data quality of the JRC Terrorism and Extremism Database for use in machine learning. It details a methodology for text classification, including text pre-processing, word vectorisation and embedding, model training and evaluation, and model deployment. The report also examines considerations for underlying data samples, text characteristics, model choices, evaluation practices, and issues of interpretability and bias.","ISSN 1831-9424  \nJRC TECHNICAL REPORT  \nJRC Terrorism and Extremism DatabaseData Quality Optimisation for Machine Learning  \nValisa, J., Bosso, F., Schumacher, R., Larcher, M.  \n2024  \nEUR 31803 EN  \nThis publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this publication do not necessarily reflect the position or opinion of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication. For information on the methodology and quality underlying the data used in this publication for which the source is neither Eurostat nor other Commission services, users should contact the referenced source. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.  \nContact information  \nName: Martin Larcher  \nEmail: martin. larcher@ec.europa.eu  \nEU Science Hub  \n[https://joint-research-centre.ec.europa.eu](https://joint-research-centre.ec.europa.eu)  \nJRC135160  \nEUR 31803 EN  \nPDF ISBN 978-92-68-11323-3 ISSN 1831-9424 doi:10.2760/919623 KJ-NA-31-803-EN-N  \nLuxembourg: Publications Office of the European Union, 2024  \n© European Union, 2024  \nThe reuse policy of the European Commission documents is implemented by the Commission Decision 2011/833/EU of 12 December 2011 on the reuse of Commission documents (OJ L 330, 14.12. 2011, p. 39). Unless otherwise noted, the reuse of this document is authorised under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)). This means that reuse is allowed provided appropriate credit is given and any changes are indicated.  \nFor any use or reproduction of photos or other material that is not owned by the European Union permission must be sought directly from the copyright holders.  \nHow to cite this report: Valisa, J., Bosso, F., Schumacher, R. and Larcher, M., JRC Terrorism and Extremism Database-Data Quality Optimisation for Machine Learning, Publications Office of the European Union, Luxembourg, 2024, doi:10.2760/919623, JRC135160 .  \nContents  \nAbstract ....................................................................................................................................................................................................................................................................... 1  \n1 Introduction..................................................................................................................................................................................................................................................... 2  \n2 Methodology .................................................................................................................................................................................................................................................. 3  \n2.1 Machine Learning for Text Classification: an Overview (2.2) ............................................................................................................3  \n2.1.1 Framework for Text Analysis and Predictive Modelling in Machine Learning.................................................4  \n2.1.1.1 Text Pre-processing ...................................................................................................................................................................................4  \n2.1.1.2 Word Vectorisation, Counting-Based Methods and Embedding ....................................................","cbCaiq0ualNepweW","https://ap.wps.com/l/cbCaiq0ualNepweW","pdf",2906913,1,34,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## Machine Learning for Text Classification: an Overview\n### Framework for Text Analysis and Predictive Modelling in Machine Learning\n### Considerations related to the Application of Machine Learning algorithms to text classification","[{\"question\":\"What is the report’s primary purpose?\",\"answer\":\"To provide evidence-based scientific support for European policymaking by improving the data quality underpinning machine learning use of the JRC Terrorism and Extremism Database.\"},{\"question\":\"Which steps are covered in the machine learning text classification approach?\",\"answer\":\"The methodology includes text pre-processing, word vectorisation/counting-based methods and embedding, model selection, performance evaluation across training/testing and hyperparameters, and model deployment.\"},{\"question\":\"What factors does the report consider when applying machine learning to text classification?\",\"answer\":\"It addresses the underlying data sample, characteristics of text data, model-related considerations, evaluation-related considerations, and issues around interpretability and bias.\"}]","JRC Terrorism and Extremism Database - 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