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More than Words: Twitter Chatter and Financial Market Sentiment. (2023). Ajello, Andrea ; Adams, Travis ; Vazquez-Grande, Francisco ; Silva, Diego.
In: Finance and Economics Discussion Series.
RePEc:fip:fedgfe:2023-34.

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  1. Monetary policy and financial markets: evidence from Twitter traffic. (2023). Romelli, Davide ; masciandaro, donato ; Rubera, Gaia.
    In: Trinity Economics Papers.
    RePEc:tcd:tcduee:tep1023.

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  46. When comparing to the BERT-based TFSI, we see a noticeable change in the shares of positive, negative, or neutral tweets when the character limit was increased in late 2017. This is likely due to finBERT’s superior ability to understand context and account for differences in the number of sentiment-laden words. These shares are relatively consistent over time with roughly 50% of tweets being classified as neutral and the remaining 50% a relatively even split between positive and negative. The linear correlation between the BERT-based and VADERbased indexes is 0.68. A.6 Twitter-based Financial Sentiment Index We construct our Financial Sentiment Index by obtaining the simple mean of the compound sentiment score for all remaining after pre-processing steps take place. We do this at weekly, and monthly frequencies. Given the higher level of variance at higher frequencies, we use the 7-day moving average of TFSI values to obtain the daily TFSI.
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