TPPI: Textual Political Polarity Indices. The Case of Italian GDP
Mathematical and Statistical Methods for Actuarial Sciences and Finance(2022)
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.
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Key words
NLP, Sentiment analysis, Text as data, Parliamentary debate, Time series
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