This shows its power, and how it is done....

http://varianceexplained.org/r/trump-tweets/

When Trump wishes the Olympic team good luck, he’s tweeting from his iPhone. When he’s insulting a rival, he’s usually tweeting from an Android. Is this an artifact showing which tweets are Trump’s own and which are by some handler?

Others have explored Trump’s timeline and noticed this tends to hold up- and Trump himself does indeed tweet from a Samsung Galaxy. But how could we examine it quantitatively? I’ve been writing about text mining and sentiment analysis recently, particularly during my development of the tidytext R package with Julia Silge, and this is a great opportunity to apply it again.

My analysis, shown below, concludes that the Android and iPhone tweets are clearly from different people, posting during different times of day and using hashtags, links, and retweets in distinct ways. What’s more, we can see that the Android tweets are angrier and more negative, while the iPhone tweets tend to be benign announcements and pictures. Overall I’d agree with @tvaziri’s analysis: this lets us tell the difference between the campaign’s tweets (iPhone) and Trump’s own (Android).
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Since we’ve observed a difference in sentiment between the Android and iPhone tweets, let’s try quantifying it. We’ll work with the NRC Word-Emotion Association lexicon, available from the tidytext package, which associates words with 10 sentiments: positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust.
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Thus, Trump’s Android account uses about 40-80% more words related to disgust, sadness, fear, anger, and other “negative” sentiments than the iPhone account does. (The positive emotions weren’t different to a statistically significant extent).