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#aBitOfCCS on neural network for scaling party positions with Hung H.V. Nguyen hosted by Jana Bernhard-Harrer
In this episode of #aBitOfCCS, Jana Bernhard-Harrer chats with Hung Nguyen, a research fellow at the German Institute for Adult Education – the Leibniz Centre for Lifelong Learning. Hung shares insights from his study, "A Sentiment-Based Approach to Measuring Multidimensional Party Positions with Transformer."
The study introduces ContextScale, a framework that uses AI to analyze party positions by separating political sentiments from rhetorical styles. Built on the XLM-RoBERTa model, ContextScale offers new ways to understand policy intentions and party dynamics across languages and domains. Hung also discusses the dataset released through his research, featuring party positions on 11 topics across 22 countries.
Join us as we explore the use of transformer models in political communication and the potential for reshaping how we analyze party positions at scale.
For more on Hung’s work, connect with him at: Hung Nguyen - [email protected]
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By Emese Domahidi & Mario HaimIn this episode of #aBitOfCCS, Jana Bernhard-Harrer chats with Hung Nguyen, a research fellow at the German Institute for Adult Education – the Leibniz Centre for Lifelong Learning. Hung shares insights from his study, "A Sentiment-Based Approach to Measuring Multidimensional Party Positions with Transformer."
The study introduces ContextScale, a framework that uses AI to analyze party positions by separating political sentiments from rhetorical styles. Built on the XLM-RoBERTa model, ContextScale offers new ways to understand policy intentions and party dynamics across languages and domains. Hung also discusses the dataset released through his research, featuring party positions on 11 topics across 22 countries.
Join us as we explore the use of transformer models in political communication and the potential for reshaping how we analyze party positions at scale.
For more on Hung’s work, connect with him at: Hung Nguyen - [email protected]