TPPI: Textual Political Polarity Indices. The Case of Italian GDP

Alessandra Amendola (), Walter Distaso () and Alessandro Grimaldi ()
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Alessandra Amendola: University of Salerno
Walter Distaso: Imperial College Business School
Alessandro Grimaldi: University of Salerno

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2022, pp 7-12 from Springer

Abstract: Abstract In this work, we propose a data-driven approach to derive a Textual Political Polarity Index (TPPI) based on the verbatim reports of the Italian “Senate of the Republic”. Our procedure allows us to build a set of polarity indices reflecting the impact of political debate and (dis)agreement within parties’ groups on a chosen economic variable - the Italian GDP growth rate - over time. Results point to a nontrivial predictive power of the proposed indices, which (importantly) do not rely on a subjective choice of an affective lexicon.

Keywords: NLP; Sentiment analysis; Text as data; Parliamentary debate; Time series (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-99638-3_2

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DOI: 10.1007/978-3-030-99638-3_2

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