We made it, you guys. Three presidential debates and we're all still standing. I don't know about you, but I am emotionally exhausted by this election, and particularly the debate performances. These debates have felt like a far cry from respectful, intellectual debate, and have each seemed to devolve into negativity and, at times, immaturity. In such a polarizing election cycle, however, it's difficult to separate one's own opinions about a candidate from his or her actual performance. As an analyst, I like to find ways to mitigate my biases - and believe me, I am quite biased this year. I decided to turn to - what else? - the data.
Sentiment Scoring
Sentiment analysis essentially checks a piece of text against a library of terms which are associated with some type and strength of emotion. There are several different types of sentiment models. The one I'm using assigns a value for how positive or negative a piece of text is. These methods are sufficiently complex to assign a positive score to "I'm happy" (0.71) and a negative score to "I'm not happy" (-0.58). The score is weighted by the total number of words in the text, so a short positive phrase will score higher than a lengthy positive phrase: "I'm a happy person" scores 0.5.
A more relevant example: "make America great again" produces a score of 0.5, whereas the phrase "stronger together" scores about 0.71. As we shall soon see, this performance of campaign slogans is just the beginning.
Debate by Debate
If we analyze a debate's sentiment by each "turn" of talk, or each segment before another participant begins speaking, we can paint a picture of the emotional progression of the debate.
First Debate
Color indicates debate participant (red for Trump, blue for Clinton, and black for moderator Holt). Size indicates how many words were spoken in the turn.
Here we see a large black dot for Holt's optimistic introduction, followed by Clinton's positive opening statement, and Trump's slightly less positive opening statement. Then we quickly start to get all over the board, with Trump showing the first dip into negative territory. And about halfway through the debate we start to see Trump's interjections of "wrong", which score -1 each. With two exceptions, Trump has the monopoly on turns scoring -0.5 or less. Conversely, Clinton was responsible for all but one +0.5 turn, and she generally seemed to be staying on the positive or neutral side. She seems to have lived up to her beloved Michelle Obama quote, "when they go low, you go high."
Second Debate
To me, this graph reveals a concerted effort from Trump to be on his best behavior. Early in the debate he had some fairly positive statements, and even had the highest scoring turn of the debate. The highest and lowest scores here are better than in the first debate, although Trump can be seen once again regressing in the second half of the debate.
Clinton, for her part, appears to have maintained her first debate performance, although she came out of the gate with a much more positive opening response to the first town hall question.
Interestingly, the moderator participation - which could be questions, follow-ups, or candidate-cat-herding - in the second debate shows a lot of quite positive turns, whereas in the first debate we saw more negatives.
Third Debate
And here we go again. Just a quarter of the way through the debate, we see Trump starting to return to the negative, short interjections. As an observer, this debate felt like the most contentious of all three, and I think that's proved out in the data by how un-crowded the neutral area is here. There's a lot more daylight in between the dots along the zero line. Clinton, although not as negative as Trump, was definitely less positive than in her first two performances.
Moderator participation looks a bit different in this debate, with a few large black dots clustered closely together toward the beginning and end.
Overall Performance
The scatterplots are interesting for us to see extreme behaviors and a little bit of trend within a single debate, but it's difficult to completely summarize how each participant came across in the debates overall. Therefore, we turn to the following bar graph, which compares each candidate (and moderator's) debate-average sentiment scores.
Numeric bar labels indicate how many sentences were spoken by the participant.
As we can see, Clinton's overall performance (the average sentiment of each of her sentences) was somewhat positive in the first debate and even more so in the second, but dipped to her least positive for the third. Trump, on the other hand, was quite close to neutral and then dipped firmly into negative territory for the second debate, and recovered slightly for the final. Trump also consistently used more sentences than Clinton, according to the transcripts. And this is not just due to short interjections like those seen so memorably in the first and final debates: Trump used 1,000-2,000 more words than Clinton in the first two debates. That gap shrunk debate by debate until finally they were almost exactly equal: 6,464 words for Trump to Clinton's 6,462.
I had assumed the debate moderators would appear the most neutral among the participants, and was surprised to see this was not the case. In particular, the town hall participants (labeled "Question") appear very positive. A lot of this is due to the low number of sentences from this group, which leaves less room for neutral, filler-type phrases. Also, kudos to Cooper and Raddatz for co-moderating on such equal footing: 144 sentences each.
In cases of sentiment, it's fun to look at the extremes. Despite being the less positive candidate in the first debate, Trump had the most positive sentence and Clinton had the most negative. This flipped in the second debate, and then Trump took both extremes in the final. You can find the phrases and other info in the table below.
| Most Positive Sentence | These are very fine institutions, very fine banks. | Well, I certainly will, because I think that's a very fair and important question. | I am a very strong supporter of the second amendment. |
| Score | 1.27 | 1.66 | 1.14 |
| Speaker | Trump | Clinton | Trump |
| Most Negative Sentence | Slashing taxes on the wealthy hasn't worked. | It's very bad, very bad health insurance. | She's guilty of a very, very serious crime. |
| Score | -1.13 | -1.66 | -1.47 |
| Speaker | Clinton | Trump | Trump |
| Trump Word Count | 8,659 | 7,364 | 6,464 |
| Clinton Word Count | 6,422 | 6,290 | 6,462 |
| Moderator Word Count | 1,918 | 2,553 | 3,286 |
I find the word count trends particularly interesting here. All three debates had between 16,000 and 17,000 words total, but the distribution changed dramatically over time, with Trump's initial dominance fading in subsequent debates as moderators participated more and more.
There you have it, folks. I hope this analysis helped bring some order to the chaos of it all. This will likely conclude any and all election coverage on this blog as I move on to areas that don't stress me out quite as much.
Here, have a kitten. I think we both need it.
Our cat Auri, the day we adopted her at three months.