Abstract
The use of keyphrases is a common oratory technique in political discourse, and politicians often guide their statements by recurrently making use of keyphrases. We propose a statistical method for extracting keyphrases at document level, combining simple heuristic rules. We show that our approach can compete with state-of-the-art systems. The method is particularly useful for the study of policy preferences and issue competition, which relies primarily on the analysis of political statements contained in party manifestos and speeches. As a case study, we show an analysis of Portuguese parliamentary debates. We extract the most used keyphrases from each parliamentary group speech collection to detect political issue emphasis. We additionally show how keyphrase clouds can be used as visualization aids to summarize the main addressed political issues.
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Notes
- 1.
- 2.
We use the POS pattern: <ADJ>* <NOUN>+.
- 3.
In our experiments we used NLTK chunker: https://www.nltk.org/api/nltk.chunk.html.
- 4.
We use the chunker POS pattern: (<NOUN>+ <ADJ>* <PREP>*)? <NOUN>+ <ADJ>*.
- 5.
- 6.
- 7.
To measure the document size we use the total number of tokens.
- 8.
- 9.
For the English datasets, the keyphrase extraction was performed with pre-trained embeddings for unigrams available in https://github.com/epfml/sent2vec. For Portuguese, we generated fastText [4] embeddings using a compilation of all sentences present in the Gerigonça dataset and the parliamentary speeches used in the case study shown in Sect. 5.
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- 11.
We manually annotated a list of approximately 100 terms.
- 12.
All keyphrases were translated from Portuguese.
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Acknowledgement
This work was supported by Fundação para a Ciência e Tecnologia (FCT), SFRH/BPD/104176/2014 to M. W., SFRH/BPD/86702/2012 to F.R., and FCT funding POCI/01/0145/FEDER/031460 and UID/CEC/50021/2019. We also want to thank the http://geringonca.com/ team for having made their data available.
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A Appendix
A Appendix
The 40 most relevant key-phrases extracted from PEV speeches collection.
The 40 most relevant keyphrases extracted from BE speeches collection.
The 40 most relevant keyphrases extracted from PCP speeches collection.
The 40 most relevant keyphrases extracted from CDS-PP speeches collection.
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Won, M., Martins, B., Raimundo, F. (2023). Automatic Extraction of Relevant Keyphrases for the Study of Issue Competition. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13452. Springer, Cham. https://doi.org/10.1007/978-3-031-24340-0_48
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