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The final Sentiment function validates the value of F P /F N and e P /e N. Depending on if the absolute value of the sentiment is greater than 25 or the absolute value of the evidence is higher than 0.5, it returns the sentiment or 0. If there are only positive words in the message, the final value of the sentiment is selected based on F P and e P only.
Therefore, the value of the sentiment feature of a text is assigned to one of three values (classes), that is, positive, negative, or neutral.
In our setting, the value of the sentiment index represents the view about the respective market.
Empirically downtoners represent intensifiers that decrease value of the sentiment by 50%%.
But the plausibility of this claim varies depending on the "distance" of the value from the sentiment, and the differences among sentiments.
Consequently, the combining function should be able to model the relation between sentences depending on the number of non-neutral words and the value of the sentiment they contain.
Open image in new window Fig. 11 Empirical p values of the sentiment bootstrap.
The ratio takes the value 1 if the sentiment strategy is the best performing strategy for the respective performance measure and lag (tau).
Rather than reversing the sentiment value we proposed to formulate a negating function that calculates the sentiment value of a negated word.
Each of these areas was individually checked carefully against maps of New York geography to characterize the reason for the sentiment value.
Section 3 gives computation method for the sentiment value of sentiment phrase with modifier, as well as sentiment intensity analysis of micro-blog content.
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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