Publicação
Cherry picking words: a topic model application to ecb speeches
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Rodrigues, Paulo Manuel Marques | |
| dc.contributor.author | Fadda, Pietro | |
| dc.date.accessioned | 2023-08-25T16:14:12Z | |
| dc.date.available | 2023-08-25T16:14:12Z | |
| dc.date.issued | 2022-06-01 | |
| dc.date.submitted | 2022-05-20 | |
| dc.description.abstract | Communication is set to become vital for policy transmission. Growing textual data availability requires the development of more sophisticated tools. This report investigated the empirical application of Latent Dirichlet Allocation (LDA), a Machine Learning Topic Model, with the aim of revealing topics in ECB speeches for the period 1997-2022. Results corroborate comparable studies and produce significant novelties: Topic dynamics captured structural macroeconomic events; ECB speakers differ in topic allocation and frequency coverage; Topic weights correlate with underlying variables and the model well perform on unseen documents. However, outcomes should be treated with caution as LDA lies between correlation and causality. | pt_PT |
| dc.identifier.tid | 203064445 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/156874 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Ecb | pt_PT |
| dc.subject | Topic modeling | pt_PT |
| dc.subject | Lda model | pt_PT |
| dc.subject | Central bank communication | pt_PT |
| dc.title | Cherry picking words: a topic model application to ecb speeches | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics | pt_PT |