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I have found this basic common expression which is misinterpreted by Vader:
`To die for.-------------- {'neg': 0.661, 'neu': 0.339, 'pos': 0.0, 'compound': -0.5994}`
Could you consider adding a …
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Looking into how we get word wise probabilty of the translated text, so as to handle poor word prediction
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Hello,
I was trying to get this working on some customer related data. None of the hacks are working although through command line I can run the sentiment analysis. Please advise as I don't want to s…
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Hi, my research into bot detection led me here – well, actually, your website; it unfortunately took me forever to actually find your GitHub (your usage of "repository" [on the overview site](https://…
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Hello,
Is there a way we can use our own lexicon in Vader sentiment? If so how can one achieve this task?
We are focusing on Sports based lexicon.
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Hi Aurelien.
I have a couple of questions on the Classification material you presented in the book and in the Jupyter note:
**1) You have outlined when one should go with the P/R model evaluatio…
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Hi, Ruidan. Thanks for sharing the code and dataset!
I find that in the evaluation.py, the calculation of sentiment F1 is as follow:
pr_s = (p_pos+p_neg+p_neu)/3.0
re_s = (r_pos+r_neg+r_n…