Leren classificeren van verkiezingsprogrammas

Geplaatst op 15-04-2014 door Maarten Marx | DiLiPaD, Political Mashup, resultaten | tags: | comment image Geen reacties »

Het artikel Automatic thematic classification of election manifestos van Suzan Verbernea, Eva D’hondt, Antal van den Bosch en Maarten Marx is verschenen in Information Processing & Management (Volume 50, Issue 4, July 2014, Pages 554–567).

Samenvatting

We digitized three years of Dutch election manifestos annotated by the Dutch political scientist Isaac Lipschits. We used these data to train a classifier that can automatically label new, unseen election manifestos with themes. Having the manifestos in a uniform XML format with all paragraphs annotated with their themes has advantages for both electronic publishing of the data and diachronic comparative data analysis. The data that we created will be disclosed to the public through a search interface. This means that it will be possible to query the data and filter them on themes and parties. We optimized the Lipschits classifier on the task of classifying election manifestos using models trained on earlier years. We built a classifier that is suited for classifying election manifestos from 2002 onwards using the data from the 1980s and 1990s. We evaluated the results by having a domain expert manually assess a sample of the classified data. We found that our automatic classifier obtains the same precision as a human classifier on unseen data. Its recall could be improved by extending the set of themes with newly emerged themes. Thus when using old political texts to classify new texts, work is needed to link and expand the set of themes to newer topics.

Bibtex

@article{Verberne2014554,
title = "Automatic thematic classification of election manifestos ",
journal = "Information Processing & Management ",
volume = "50",
number = "4",
pages = "554 - 567",
year = "2014",
note = "",
issn = "0306-4573",
doi = "http://dx.doi.org/10.1016/j.ipm.2014.02.006",
url = "http://www.sciencedirect.com/science/article/pii/S0306457314000168",
author = "Suzan Verberne and Eva D’hondt and Antal van den Bosch and Maarten Marx",
keywords = "Text classification",
keywords = "Political data",
keywords = "Expert evaluation "
}