How IBM’s Watson scores the tone of Trump’s tweets

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"Donald Trump by Gage Skidmore 3" by Gage Skidmore. Licensed under CC BY-SA 3.0 via Commons.

Donald Trump by Gage Skidmore 3” by Gage Skidmore. Licensed under CC BY-SA 3.0 via Commons.

Such a beautiful and important evening! The forgotten man and woman will never be forgotten again. We will all come together as never before

— Donald J. Trump (@realDonaldTrump) November 9, 2016

That is Donald Trump’s most-retweeted tweet since he was elected president. He sounds happy, right? Not so much, according to IBM’s Watson. The artificial intelligence software’s Tone Analyzer tool measured the tweet’s tone as more likely to be perceived as sad than joyful, as shown on the chart below.

TrumpWatsonTweets_tweetForgotten-1920

Let’s try his second most-retweeted, post-election tweet.

Happy New Year to all, including to my many enemies and those who have fought me and lost so badly they just don’t know what to do. Love!

— Donald J. Trump (@realDonaldTrump) December 31, 2016

Too bad passive-aggressive isn’t an option. Of the five available emotions — anger, disgust, fear, joy and sadness (like the characters in “Inside Out”) — let’s go with joy; he did say “Happy New Year” and “Love” with an exclamation. Right again, says Watson, which scored the tweet as much more likely to be perceived as joyful than any of the other four emotions.

TrumpWatsonTweets_tweetNewYear-1920

Okay, now his third most-retweeted, post-election tweet.

Fidel Castro is dead!

— Donald J. Trump (@realDonaldTrump) November 26, 2016

Well, death proclamations are usually sad, but they’re not usually exclamatory. I can cheat by knowing that Trump followed up this tweet by calling the late Cuban leader “a brutal dictator.” So he’s probably happy about his death. Joy, lock it in. Nope, knock it off, says Watson. The tweet is actually highly likely to be perceived as sad and not likely at all to be considered joyful (or angry or disgusted or scared).

TrumpWatsonTweets_tweetCastro-1920

Let’s try his fourth most-retweeted tweet.

Nobody should be allowed to burn the American flag – if they do, there must be consequences – perhaps loss of citizenship or year in jail!

— Donald J. Trump (@realDonaldTrump) November 29, 2016

Of the five emotional options, I’d say Trump sounds disgusted the most. And according to Watson, I’d be right. But I’d be even more right if I also sensed a little sadness, as IBM’s artificial intelligence technology did.

TrumpWatsonTweets_tweetFlag-1920

Let’s finish with number five.

Just had a very open and successful presidential election. Now professional protesters, incited by the media, are protesting. Very unfair!

— Donald J. Trump (@realDonaldTrump) November 11, 2016

Angry, definitely angry. And definitely wrong, says Watson. While Watson measured this tweet as less likely to be perceived as any of the five emotions, it scored it as much more likely to be perceived as sad than angry, disgusted, scared or joyful.

TrumpWatsonTweets_tweetProtesters-1920

So what does any of this mean? Am I wrong because I’m not judging the tweet solely on its text, while Watson did? Is Watson wrong because, smart as artificial intelligence software has become, it can’t intuit emotion at a human level? To be honest, I’m not sure, and IBM declined to participate in this story.

Artificial intelligence has become a popular topic in the marketing industry, dominating discussion going into and coming out of this year’s CES. And artificial intelligence software is being pitched to marketers to use for everything from powering their Facebook Messenger bots to analyzing customer service complaints to predicting how people might respond to a brand’s tweet. With artificial intelligence gaining in importance, it seems like as good a time as any to gauge its insight.

According to case studies on IBM’s site, Watson’s Tone Analyzer tool can be used by brands to work out whether a potential tweet is more or less likely to get retweeted and liked as well as by public speakers to ascertain whether people will like their TED talk. I decided to apply the tool to Donald Trump’s tweets.

In addition to the aforementioned five most popular, post-election tweets, I took @realDonaldTrump’s entire corpus since he was formally named the Republican presidential candidate in July 2016, and I ran the tweets’ text through Watson’s Tone Analyzer to study the tone of Trump’s tweets, to see if it had changed in the transition from presidential candidate to president-elect and, if so, how.

In general, Watson scored Trump’s tweets as being primarily joyful, emotional, compassionate, thoughtful and not exerting certainty or inhibition. And aside from being slightly angrier, the president-elect’s Twitter tone hasn’t changed much. Like I said above, I’m not sure what to make of Watson’s results. So I’ll leave it up to you to judge for yourself by checking out the charts below.

But before you do, a quick explanation of my methodology. Because Watson’s Tone Analyzer has a limit on how much text it can process at a time, I separated the tweets into three time periods 1) from the day after being nominated to the day before the first presidential debate, 2) the day of the first presidential debate until Election Day and 3) the day after Election Day until yesterday. These time periods seemed to be the clearest stages in which the tone of Trump’s tweets may have changed, from the freshly nominated candidate to the candidate in the final throes of a tumultuous election to the president-elect.

After running the text for each period through Tone Analyzer, I plotted the scores into charts for each tonal category to compare the results. Each chart also features short explanations of the category that are culled from IBM’s site.

The scores are provided on a scale from 0.00 to 1.00. Generally the higher the score, the more likely the text is to be perceived as having a given tone. For example, a text scoring 1.00 in “Joy” is considered highly likely to come across as joyful, whereas a text scoring 0.00 isn’t very likely to come across as joyful at all. And a text scoring a 1.0 in “Agreeableness” is likely to be perceived caring or humble, whereas a 0.00 is more likely to be perceived as selfish or arrogant.

EMOTIONAL TONE

These categories measure the feelings being expressed, such as how joyful or how angry a text is. Overall, Trump’s tweets were judged to be more joyful than any other emotion and not very angry or disgusted.

TrumpTweetsWatson_Anger-1920TrumpTweetsWatson_Disgust-1920TrumpTweetsWatson_Fear-1920TrumpTweetsWatson_Joy-1920TrumpTweetsWatson_Sadness-1920

SOCIAL TONE

These categories measure the personality traits exhibited, such as how sensitive or thoughtful the person who wrote the text is. Overall, Trump’s tweets were judged to be emotional, compassionate and thoughtful.

TrumpTweetsWatson_Agreeableness-1920TrumpTweetsWatson_Conscientiousness-1920TrumpTweetsWatson_EmotionalRange-1920TrumpTweetsWatson_Extraversion-1920TrumpTweetsWatson_Openness-1920

LANGUAGE TONE

These categories measure writing style, such as whether a text is analytical and whether it is written with certainty. Overall, Trump’s tweets were judged to not exert certainty or inhibition.

TrumpTweetsWatson_Analytic-1920TrumpTweetsWatson_Confidence-1920TrumpTweetsWatson_Tentative-1920


About The Author

Tim Peterson, Third Door Media’s Social Media Reporter, has been covering the digital marketing industry since 2011. He has reported for Advertising Age, Adweek and Direct Marketing News. A born-and-raised Angeleno who graduated from New York University, he currently lives in Los Angeles. He has broken stories on Snapchat’s ad plans, Hulu founding CEO Jason Kilar’s attempt to take on YouTube and the assemblage of Amazon’s ad-tech stack; analyzed YouTube’s programming strategy, Facebook’s ad-tech ambitions and ad blocking’s rise; and documented digital video’s biggest annual event VidCon, BuzzFeed’s branded video production process and Snapchat Discover’s ad load six months after launch. He has also developed tools to monitor brands’ early adoption of live-streaming apps, compare Yahoo’s and Google’s search designs and examine the NFL’s YouTube and Facebook video strategies.


 

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