Publicação
Quantifying implicit political intentions in parliamentary discourse - an integrated approach using semi-supervised learning and vector space information retrieval
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Zejnilovic, Leid | |
| dc.contributor.author | Sturm, Niclas Frederic | |
| dc.date.accessioned | 2022-06-17T13:10:03Z | |
| dc.date.available | 2022-06-17T13:10:03Z | |
| dc.date.issued | 2022-01-20 | |
| dc.date.submitted | 2021-12-15 | |
| dc.description.abstract | Recent advances in Natural Language Processing and Information Retrieval have opened a new world of possibilities for the analysis of text. This study seeks to explore the possibilities of applying these techniques on political text, with a focus on quantifying intentions in parliamentary speeches and activities in Portugal. Combining vector space models and semi-supervised learning, a semantic search engine is able to extract meaningful metrics from text that help to identify political trends and quantify alignment with political issues. | pt_PT |
| dc.identifier.tid | 202997375 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/140155 | |
| dc.language.iso | eng | pt_PT |
| dc.relation | Nova School of Business and Economics | |
| dc.subject | Natural language processing | pt_PT |
| dc.subject | Political science | pt_PT |
| dc.subject | Semi-supervised learning | pt_PT |
| dc.subject | Informational retrieval | pt_PT |
| dc.subject | Discourse analysis | pt_PT |
| dc.subject | Business analysis | pt_PT |
| dc.title | Quantifying implicit political intentions in parliamentary discourse - an integrated approach using semi-supervised learning and vector space information retrieval | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| oaire.awardNumber | UID/ECO/00124/2013 | |
| oaire.awardTitle | Nova School of Business and Economics | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FECO%2F00124%2F2013/PT | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| relation.isProjectOfPublication | 644a3f4f-817b-4d0d-aba6-f98cdca28bc7 | |
| relation.isProjectOfPublication.latestForDiscovery | 644a3f4f-817b-4d0d-aba6-f98cdca28bc7 | |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Masters Double Degree in Management and International Business from the NOVA – School of Business and Economics and Maastricht University Faculty of Economics and Business Administration | pt_PT |
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