Multi-aspect multilingual and cross-lingual parliamentary speech analysis

Author:

Miok Kristian1,Hidalgo Tenorio Encarnación2,Osenova Petya34,Benítez-Castro Miguel-Ángel5,Robnik-Šikonja Marko6

Affiliation:

1. ICAM – Advanced Environmental Research Institute, West Unversity of Timisoara, Romania

2. Department of English and German Studies, Facultad de Filosofía y Letras, Universidad de Granada, Calle Prof. Clavera s/n, Granada, Spain

3. Faculty of Slavic Studies, Sofia University St. Kl. Ohridski, Sofia, Bulgaria

4. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, The Republic of Bulgaria

5. Department of English and German Studies, Faculty of Social Sciences and Humanities, University of Zaragoza, Calle Ciudad Escolar, Teruel, Spain

6. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

Abstract

Parliamentary and legislative debate transcripts provide an informative insight into elected politicians’ opinions, positions, and policy preferences. They are interesting for political and social sciences as well as linguistics and natural language processing (NLP) research. While exiting research studied individual parliaments, we apply advanced NLP methods to a joint and comparative analysis of six national parliaments (Bulgarian, Czech, French, Slovene, Spanish, and United Kingdom) between 2017 and 2020. We analyze emotions and sentiment in the transcripts from the ParlaMint dataset collection, and assess if the age, gender, and political orientation of speakers can be detected from their speeches. The results show some commonalities and many surprising differences among the analyzed countries.

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Theoretical Computer Science

Reference42 articles.

1. G. Abercrombie and R.T. Batista-Navarro, ParlVote: A corpus for sentiment analysis of political debates, in: Proceedings of the 12th Language Resources and Evaluation Conference, 2020, pp. 5073–5078.

2. C.O. Alm, D. Roth and R. Sproat, Emotions from text: machine learning for text-based emotion prediction, in: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, 2005, pp. 579–586.

3. Gender, genre, and writing style in formal written texts;Argamon;Text & Talk,2003

4. A dataset for sentiment analysis of entities in news headlines (SEN);Baraniak;Procedia Computer Science,2021

5. Annotated news corpora and a lexicon for sentiment analysis in Slovene;Bučar;Language Resources and Evaluation,2018

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