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  A New Database for Italian Parliamentary Speeches: Introducing the ItaParlCorpus Dataset

Cova, J. (2025). A New Database for Italian Parliamentary Speeches: Introducing the ItaParlCorpus Dataset. Italian Political Science Review. doi:10.1017/ipo.2025.6.

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https://doi.org/10.1017/ipo.2025.6 (Publisher version)
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https://doi.org/10.7910/DVN/KUARWD (Research data)
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 Creators:
Cova, Joshua1, Author                 
Affiliations:
1Politische Ökonomie, MPI for the Study of Societies, Max Planck Society, ou_3363015              

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Free keywords: Italy, parliament, political parties, research methods, text analysis
 Abstract: A common challenge in studying Italian parliamentary discourse is the lack of accessible, machine-readable, and systematized parliamentary data. To address this, this article introduces the ItaParlCorpus dataset, a new, annotated, machine-readable collection of Italian parliamentary plenary speeches for the Camera dei Deputati, the lower house of Parliament, spanning from 1948 to 2022. This dataset encompasses 470 million words and 2.4 million speeches delivered by 5830 unique speakers representing 77 different political parties. The files are designed for easy processing and analysis using widely-used programming languages, and they include metadata such as speaker identification and party affiliation. This opens up opportunities for in-depth analyses on a variety of topics related to parliamentary behavior, elite rhetoric, and the salience of political themes, exploring how these vary across party families and over time.

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Language(s): eng - English
 Dates: 2025-02-162024-09-032025-02-182025-03-14
 Publication Status: Published online
 Pages: 10
 Publishing info: -
 Table of Contents: Introduction
Parliamentary speeches in big data and natural language processing research
The dataset
Potential applications
Conclusion
Data
Footnotes
References
 Rev. Type: -
 Identifiers: DOI: 10.1017/ipo.2025.6
 Degree: -

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Source 1

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Title: Italian Political Science Review
  Other : Rivista Italiana di Scienza Politica
Source Genre: Journal
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 0048-8402
ISSN: 2057-4908