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This story was originally published on HackerNoon at: https://hackernoon.com/a-novel-framework-for-analyzing-economic-news-narratives-using-gpt-35-data.
Analyzing economic news with GPT-3.5 and network analysis to detect evolving topics and narratives, and linking news structures to financial market volatility.
Check more stories related to finance at: https://hackernoon.com/c/finance. You can also check exclusive content about #financial-markets, #ai-in-finance, #economic-news-analysis, #hedging-strategies, #gpt-3.5-applications, #sentiment-analysis, #network-analysis, #financial-market-volatility, and more.
This story was written by: @hedging. Learn more about this writer by checking @hedging's about page, and for more stories, please visit hackernoon.com.
We download a tractable corpus of news from Factiva1 data provider by looking for news written in English. We consider approximately four years of daily news, i.e. from January 2020 to October 2023, and aggregate them at weekly level. After pre-processing them to a standard format, we achieve a dataset of 197 weeks with a total of 21, 590 news, with 110 data points per week.
143 قسمت
This story was originally published on HackerNoon at: https://hackernoon.com/a-novel-framework-for-analyzing-economic-news-narratives-using-gpt-35-data.
Analyzing economic news with GPT-3.5 and network analysis to detect evolving topics and narratives, and linking news structures to financial market volatility.
Check more stories related to finance at: https://hackernoon.com/c/finance. You can also check exclusive content about #financial-markets, #ai-in-finance, #economic-news-analysis, #hedging-strategies, #gpt-3.5-applications, #sentiment-analysis, #network-analysis, #financial-market-volatility, and more.
This story was written by: @hedging. Learn more about this writer by checking @hedging's about page, and for more stories, please visit hackernoon.com.
We download a tractable corpus of news from Factiva1 data provider by looking for news written in English. We consider approximately four years of daily news, i.e. from January 2020 to October 2023, and aggregate them at weekly level. After pre-processing them to a standard format, we achieve a dataset of 197 weeks with a total of 21, 590 news, with 110 data points per week.
143 قسمت
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