Quantifying the Trump-iness of Political Sentences

You could say that Donald Trump has a… distinct way of speaking. He doesn’t talk the way other politicians do (even ignoring his accent), and the contrast between him and Clinton is pretty strong. But can we figure out what differentiates them? And then, can we find the most… Trump-ish sentence?

That was the challenge my friend Spencer posed to me as my first major foray into data science, the new career I’m starting. It was the perfect project: fun, complicated, and requiring me to learn new skills along the way.

To find out the answers, read on! The results shouldn’t be taken too seriously, but they’re amusing and give some insight into what might be important to each candidate and how they talk about the political landscape. Plus, it serves to demonstrate the data science techniques I’m learning for as a portfolio project.

If you want to play with the model yourself, I also put together an interactive javascript page for you: you can test your judgment compared to its predictions, browse the most Trumpish/Clintonish sentences and terms, and enter your own text for the model to evaluate.

To read about how the model works, I wrote a rundown with both technical and non-technical details below the tables and graphs. But without further ado, the results:

The Trump-iest and Clinton-est Sentences and Phrases from the 2016 Campaign:

Other Fun Results:

Cherrypicked pairs of terms:

How the Model Works:

Defining the problem: What makes a sentence “Trump-y?”

I decided that the best way to quantify ‘Trump-iness’ of a sentence was to train a model to predict whether a given sentence was said by Trump or Clinton. The Trumpiest sentence will be the one that the predictive model would analyze and say “Yup, the chance this was Trump rather than Clinton is 99.99%”.

Along the way, with the right model, we can ‘look under the hood’ to see what factors into the decision.

Technical details:

The goal is to build a classifier that can distinguish between the candidate’s sentences optimizing for ROC_AUC, and allows us to extract meaningful/explainable coefficients.

Gathering and processing the data:

In order to train the model, I needed large bodies of text from each candidate. I ended up scraping transcripts from events on C-SPAN.org. Unfortunately, they’re uncorrected closed caption transcripts and contained plenty of typos and misattributions. On the other hand, they’re free.

I did a bit to clean up some recurring problems like the transcript starting every quote section with “Sec. Clinton:” or including descriptions like [APPLAUSE] or [MUSIC]. (Unfortunately, they don’t reliably mark the end of the music, and C-SPAN sometimes claims that Donald Trump is the one singing ‘You Can’t Always Get What You Want.’)

Technical details:

I ended up learning to use Python’s Beautiful Soup library to identify the list of videos C-SPAN considers campaign events by the candidates, find their transcripts, and grab only the parts they supposedly said. I learned to use some basic regular expressions to do the cleaning.

My scraping tool is up on github, and is actually configured to be able to grab transcripts for other people as well.

Converting the data into usable features

After separating the large blocks of text into sentences and then words, I had some decisions to make. In an effort to focus on interesting and meaningful content, I removed sentences that were too short or too long – “Thank you” comes up over and over, and the longest sentences tended to be errors in the transcription service. It’s a judgement call, but I wanted to keep half the sentences, which set cutoffs at 9 words and 150 words. 34,108 sentences remained.

A common technique in natural language processing is to remove the “stopwords” – common non-substantive words like articles (a, the), pronouns (you, we), and conjunctions (and, but). However, following James Pennebaker’s research, which found these words are surprisingly useful in predicting personality, I left them in.

Now we have what we need: sequences of words that the model can consider evidence of Trump-iness.

Technical details:

I used NLTK to tokenize the text into sentences, but wrote my own regular expressions to tokenize the words. I considered it important to keep contractions together and include single-character tokens, which the standard NLTK function wouldn’t have done.

I used a CountVectorizer from sklearn to extract ngrams and later selected the most important terms using a SelectFromModel with a Lasso Logistic Regression. It was a balance – more terms would typically improve accuracy, but water down the meaningfulness of each coefficient.

I tested using various additional features, like parts of speech and lemmas (using the fantastic Spacy library) and sentiment analysis (using the Textblob library) but found that they only provided marginal benefit and made the model much slower. Even just using 1-3 ngrams, I got 0.92 ROC_AUC.

Choosing & Training the Model

One of the most interesting challenges was avoiding overfitting. Without taking countermeasures, the model could look at a typo-riddled sentence like “Wev justv don’tv winv anymorev.” and say “Aha! Every single one of those words are unique to Donald Trump, therefore this is the most Trump-like sentence ever!”

I addressed this problem in two ways: the first is by using regularization, a standard machine learning technique that penalizes a model for using larger coefficients. As a result, the model is discouraged from caring about words like ‘justv’ which might only occur two times, since they would only help identify those couple sentences. On the other hand, a word like ‘frankly’ helps identify many, many sentences and is worth taking a larger penalty to give it more importance in the model.

The other technique was to use batch predictions – dividing the sentences into 20 chunks, and evaluating each chunk by only training on the other 19. This way, if the word ‘winv’ only appears in a single chunk, the model won’t see it in the training sentences and won’t be swayed. Only words that appear throughout the campaign have a significant impact in the model.

Technical details:

The model uses a logistic regression classifier because it produces very explainable coefficients. If that weren’t a factor, I might have tried a neural net or SVM (I wouldn’t expect a random forest to do well with such sparse data.) In order to set the regularization parameters for both the final classifier and for the feature-selection Lasso Logistic Regressor, I used sklearn’s cross-validated gridsearch object, optimizing for ROC_AUC.

During the prediction process, I used a stratified Kfold to divide the data in order to ensure each chunk would have the appropriate mix of Trump and Clinton sentences. It was tempting to treat the sentences more like a time series and only use past data in the predictions, but we want to consider how similar old sentences are to the whole corpus.

Interpreting and Visualizing the Results:

The model produced two interesting types of data: how likely the model thought each sentence was spoken by Trump or Clinton (how ‘Trumpish’ vs. ‘Clintonish’ it is), and how any particular term impacts those predicted odds. So if a sentence is predicted to be spoken by Trump with estimated 99.99% probability, the model considers it extremely Trumpish.

The term’s multipliers indicate how each word or phrase impacts the predicted odds. The model starts at 1:1 (50%/50%), and let’s say the sentence includes the word “incredible” – a Trump multiplier of 7.42. The odds are now 7.42 : 1, or roughly 88% in favor of Trump. If the model then sees the word “grandmother” – a Clinton multiplier of 6.12 – its estimated odds become 7.42 : 6.12, (or 1.12 : 1), roughly 55% Trump. Each term has a multiplying effect, so a 4x word and 2x word together have as much impact as an 8x word – not 6x.

Technical details:

In order to visualize the results, I spent a bunch of time tweaking the matplotlib package to generate a graph of coefficients, which I used for the pronouns above. I made sure to use a logarithmic scale, since the terms are multiplicative.

In addition, I decided to teach myself enough javascript to learn to use the D3 library – allowing interactive visualizations and the guessing game where players can try to figure out who said a given random sentence from the campaign trail. There are a lot of ways the code could be improved, but I’m pleased with how it turned out given that I didn’t know any D3 prior to this project.

Pick a name for a rationality non-profit!

My new job is basically my dream job: I just moved to the Bay area to help launch a non-profit devoted to teaching rationality.

But we need your help settling on a name. We’ve got it narrowed down to three contenders; click here to vote for your favorite. Thanks!

My kind of protest sign

Via Occupy Sanity, on Facebook:

And how about a: “Two, four, six, eight! And if you could please register your studies ahead of time to combat publication bias, that would be great!”

Thoughts on science podcasting: A dispatch from ScienceOnline 2012

I’m in Raleigh, NC this weekend for the sixth annual ScienceOnline un-conference, a gathering of 450 scientists, writers, bloggers, podcasters, educators, and others interested in the way the internet is changing the way we conduct, and communicate, science. My contribution was this morning — I moderated  a discussion on science podcasting with Desiree Schell, the eloquent host of Skeptically Speaking. We made a nicely complementary team. Her podcast is live, whereas mine is pre-recorded; hers is solo; whereas mine is a dialogue with a co-host; hers focuses on the practical applications of science to people’s lives and pocketbooks (e.g., the common cold, the claims of the cosmetics industry, etc.), whereas mine is more abstract and philosophical. So our combined perspectives overlaid together created a kind of podcasting guide in 3D.

A few highlights:

What’s your niche? There are a lot of science and skepticism podcasts out there already, and Desiree and I both agreed that you need a well-defined “niche” in mind if you’re going to start your own. Maybe it’s a topic  you think isn’t being covered enough, or it’s not being covered the way you think it should be, or maybe it’s a group you want to give a voice to. But there should be some reason your podcast exists other than the fact that you want to do a podcast.

For example, I consider Rationally Speaking’s niche to be in the philosophical implications of science. So instead of just covering topics like irrationality, or the science of love, we also try to hash out questions like, Why should we try to overcome irrationality? Does it actually make us happier, and what are the ethical implications of trying to make other people more rational? And if we understand the science of love, does that change our experience of love?

And then our other niche is the question of what constitutes good evidence for a claim: To what extent do fields like evolutionary psychology, string theory, and memetics make testable predictions, and if they don’t, can we have any confidence in their claims? Can we ever generalize from case studies? How do we know which experts to trust?  A lot of skeptic podcasts and blogs highlight claims that are unambiguously pseudoscience, but I think Rationally Speaking specializes in the murkier cases.

The outline versus the map: Desiree and I talked a lot about how to make podcast interviews and conversations go smoothly. When I first started doing Rationally Speaking, I would come into our tapings with a mental outline of the topics I wanted to cover, arranged in a nice order that flowed well… and as it turns out, that’s fine for when you’re giving a lecture, solo, but it just doesn’t work when you throw other people into the mix. You don’t know what topics your guest is going to bring up that call for follow-up, and I never know what direction Massimo’s going to take the conversation in. And the problem with  having an outline in your head is that once you diverge from that outline, you have no instructions for how to get back onto it.

So what I’ve settled on instead is more of a loose, web-like structure in my mind, where the topics aren’t in any set order, but for each topic, I’ve thought about how it connects to at least a couple of other topics. That way, wherever the conversation ends up, I have this map in my head of where I can go next.

Why a podcast at all? For that matter, you should really have a reason to do a podcast rather than write a blog. Podcasts have some significant downsides, compared to blogs. On the production end, they’re a hassle to record and edit, compared to writing a post, and they commit you to a specific length and schedule. On the consumption end, they’re inconvenient in that you can’t skim them at your own pace, you can’t skip down to another section, and you don’t get links or pictures to supplement the content.

But sometimes they really are better than a blog post. I think that’s especially true for treating controversial or multifaceted topics, the kind we look for in Rationally Speaking – hearing people debate a topic is far more engaging than reading one person’s point of view. Also, as Story Collider’s Ben Lillie pointed out during the conversation, listening to a science podcast creates an intimate connection to the scientist – when you’ve got headphones on and you’re hearing the scientist’s voice as if she’s right there with you, it takes barely any time to get a taste of her personality. And science could always use a little more humanizing.

RS#40: Q&A with Massimo and Julia

The latest episode of the Rationally Speaking podcast is a special one-hour Q&A, in which Massimo and I answer selected questions from our listeners. In this installment the topics include: What would we teach in a class on critical thinking? What are our views on on analytic vs. continental philosophy? Is it ethical to take advantage of a drought to sell water at marked-up prices? How do we compartmentalize our rationality? How does modern technology affect the way we think about things? What is, or should be, the primary purpose of our species? And is there really a rational argument to prove the divine origin of the Bible?

http://www.rationallyspeakingpodcast.org/show/rs40-qa-with-massimo-and-julia.html

The Ethics of Paranormal Investigation, Part 2 of 2

A continuation of my discussion about the panel I moderated at The Amazing Meeting, on the ethics of paranormal investigation:

The Ethics of Paranormal Investigation, Part 1 of 2

Today we end our first hiatus (which turned out to by much longer than we’d planned — apologies, dear readers!) with the first of a two-part video blog. It’s a discussion of the panel I moderated at TAM9 this year, titled “The Ethics of Paranormal Investigation.” Topics include: Is it acceptable to use deception in the course of unmasking someone else’s deception? What do you do when the person claiming paranormal powers is a child? How much do you feel obligated to ensure the confidentiality of the people you’re investigating? And do you have any responsibility, as a mentalist or magician, to make sure your audience understands what you’re doing isn’t real?