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In Figure 7, we plot the sentiment time series for Google Books.
Figure 8 Normalized sentiment time series on Twitter using (pmb{Delta_{h}}) of 1.0 for all dictionaries.
In Figure 8, we present a daily sentiment time series of Twitter processed using each of the dictionaries being tested.
The sentiment time series as well as the 200 bootstrapped time series are incorporated in the COP framework separately.
Generally, we see strong agreement within dictionaries with varying stop values Δh. Figure 9 Pearson's r correlation between daily resolution Twitter sentiment time series for each sentiment dictionary.
For each of the 200 bootstrap sentiment time series, the sentiment strategy is applied in the training and the test period separately.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com