The newest stan.suggest.polarity worthy of signifies the fresh new standardized imply polarity, the mediocre polarity split up of the simple departure. 267) than just 2010 (0.121). This really is in line with whatever you manage expect, wanting to avoid into a far more confident mention. You can even plot the information and knowledge. The brand new plot provides one or two charts. The first shows this new polarity from the phrases through the years therefore the second suggests new shipment of your polarity: > plot(pol)

This patch can be a challenge to read within text, however, let me manage my personal best to translate they. The newest 2010 address begins with an effective negative sentiment and you can is actually somewhat way more negative than 2016. We can identify the essential bad belief phrase by making a good dataframe of pol target, get the phrase count, and produce it: > pol.df and that.min(pol.df$polarity) a dozen > pol.df$text message.var “12 months before, I took workplace in the middle of a couple of wars, a savings rocked by the an extreme market meltdown, a financial system to the brink out-of failure, and you may an authorities deeply with debt.

Since try negative sentiment! Ironically, the government is even a whole lot more indebted now. We’re going to glance at the readability index 2nd: > ari ari$Readability year term.matter sentence.amount character.amount step one 2010 3900 435 23859 2 2016 2982 299 17957 Automated_Readability_Index step one 9 2 9

This proves the brand new dispersion out-of a term throughout the text message

I believe it is no shock that they are essentially the exact same. Foregone conclusion data was next.

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