Text analysis of Trump tweets

Introduction

This post is to proclaim the MathematicaVsR at GitHub project “Text analysis of Trump tweets” in which we compare Mathematica and R over text analyses of Twitter messages made by Donald Trump (and his staff) before the USA president elections in 2016.

The project follows and extends the exposition and analysis of the R-based blog post "Text analysis of Trump’s tweets confirms he writes only the (angrier) Android half" by David Robinson at VarianceExplained.org; see [1].

The blog post [1] links to several sources that claim that during the election campaign Donald Trump tweeted from his Android phone and his campaign staff tweeted from an iPhone. The blog post [1] examines this hypothesis in a quantitative way (using various R packages.)

The hypothesis in question is well summarized with the tweet:

Every non-hyperbolic tweet is from iPhone (his staff).
Every hyperbolic tweet is from Android (from him). pic.twitter.com/GWr6D8h5ed
— Todd Vaziri (@tvaziri) August 6, 2016

This conjecture is fairly well supported by the following mosaic plots, [2]:

TextAnalysisOfTrumpTweets-iPhone-MosaicPlot-Sentiment-Device TextAnalysisOfTrumpTweets-iPhone-MosaicPlot-Device-Weekday-Sentiment

We can see the that Twitter messages from iPhone are much more likely to be neutral, and the ones from Android are much more polarized. As Christian Rudder (one of the founders of OkCupid, a dating website) explains in the chapter "Death by a Thousand Mehs" of the book "Dataclysm", [3], having a polarizing image (online persona) is as a very good strategy to engage online audience:

[…] And the effect isn’t small-being highly polarizing will in fact get you about 70 percent more messages. That means variance allows you to effectively jump several "leagues" up in the dating pecking order – […]

(The mosaic plots above were made for the Mathematica-part of this project. Mosaic plots and weekday tags are not used in [1].)

Concrete steps

The Mathematica-part of this project does not follow closely the blog post [1]. After the ingestion of the data provided in [1], the Mathematica-part applies alternative algorithms to support and extend the analysis in [1].

The sections in the R-part notebook correspond to some — not all — of the sections in the Mathematica-part.

The following list of steps is for the Mathematica-part.

  1. Data ingestion
    • The blog post [1] shows how to do in R the ingestion of Twitter data of Donald Trump messages.

    • That can be done in Mathematica too using the built-in function ServiceConnect, but that is not necessary since [1] provides a link to the ingested data used [1]:
      load(url("http://varianceexplained.org/files/trump_tweets_df.rda"))

    • Which leads to the ingesting of an R data frame in the Mathematica-part using RLink.

  2. Adding tags

    • We have to extract device tags for the messages — each message is associated with one of the tags "Android", "iPad", or "iPhone".

    • Using the message time-stamps each message is associated with time tags corresponding to the creation time month, hour, weekday, etc.

    • Here is summary of the data at this stage:

    "trumpTweetsTbl-Summary"

  3. Time series and time related distributions

    • We can make several types of time series plots for general insight and to support the main conjecture.

    • Here is a Mathematica made plot for the same statistic computed in [1] that shows differences in tweet posting behavior:

    "TimeSeries"

    • Here are distributions plots of tweets per weekday:

    "ViolinPlots"

  4. Classification into sentiments and Facebook topics

    • Using the built-in classifiers of Mathematica each tweet message is associated with a sentiment tag and a Facebook topic tag.

    • In [1] the results of this step are derived in several stages.

    • Here is a mosaic plot for conditional probabilities of devices, topics, and sentiments:

    "Device-Topic-Sentiment-MosaicPlot"

  5. Device-word association rules

    • Using Association rule learning device tags are associated with words in the tweets.

    • In the Mathematica-part these associations rules are not needed for the sentiment analysis (because of the built-in classifiers.)

    • The association rule mining is done mostly to support and extend the text analysis in [1] and, of course, for comparison purposes.

    • Here is an example of derived association rules together with their most important measures:

    "iPhone-Association-Rules"

In [1] the sentiments are derived from computed device-word associations, so in [1] the order of steps is 1-2-3-5-4. In Mathematica we do not need the steps 3 and 5 in order to get the sentiments in the 4th step.

Comparison

Using Mathematica for sentiment analysis is much more direct because of the built-in classifiers.

The R-based blog post [1] uses heavily the "pipeline" operator %>% which is kind of a recent addition to R (and it is both fashionable and convenient to use it.) In Mathematica the related operators are Postfix (//), Prefix (@), Infix (~~), Composition (@*), and RightComposition (/*).

Making the time series plots with the R package "ggplot2" requires making special data frames. I am inclined to think that the Mathematica plotting of time series is more direct, but for this task the data wrangling codes in Mathematica and R are fairly comparable.

Generally speaking, the R package "arules" — used in this project for Associations rule learning — is somewhat awkward to use:

  • it is data frame centric, does not work directly with lists of lists, and

  • requires the use of factors.

The Apriori implementation in “arules” is much faster than the one in “AprioriAlgorithm.m” — “arules” uses a more efficient algorithm implemented in C.

References

[1] David Robinson, "Text analysis of Trump’s tweets confirms he writes only the (angrier) Android half", (2016), VarianceExplained.org.

[2] Anton Antonov, "Mosaic plots for data visualization", (2014), MathematicaForPrediction at WordPress.

[3] Christian Rudder, Dataclysm, Crown, 2014. ASIN: B00J1IQUX8 .

Handwritten digits recognition by matrix factorization

Introduction

This MathematicaVsR at GitHub project is for comparing Mathematica and R for the tasks of classifier creation, execution, and evaluation using the MNIST database of images of handwritten digits.

Here are the bases built with two different classifiers:

  • Singular Value Decomposition (SVD)

SVD-basis-for-5

  • Non-Negative Matrix Factorization (NNMF)

NNMF-basis-for-5

Here are the confusion matrices of the two classifiers:

  • SVD

SVD-confusion-matrix

  • NNMF

NNMF-confusion-matrix

The blog post "Classification of handwritten digits" (published 2013) has a related more elaborated discussion over a much smaller database of handwritten digits.

Concrete steps

The concrete steps taken in scripts and documents of this project follow.

  1. Ingest the binary data files into arrays that can be visualized as digit images.
  • We have two sets: 60,000 training images and 10,000 testing images.
  1. Make a linear vector space representation of the images by simple unfolding.

  2. For each digit find the corresponding representation matrix and factorize it.

  3. Store the matrix factorization results in a suitable data structure. (These results comprise the classifier training.)

  • One of the matrix factors is seen as a new basis.
  1. For a given test image (and its linear vector space representation) find the basis that approximates it best. The corresponding digit is the classifier prediction for the given test image.

  2. Evaluate the classifier(s) over all test images and compute accuracy, F-Scores, and other measures.

Scripts

There are scripts going through the steps listed above:

Documents

The following documents give expositions that are suitable for reading and following of steps and corresponding results.

Observations

Ingestion

I figured out first in R how to ingest the data in the binary files of the MNIST database. There were at least several online resources (blog posts, GitHub repositories) that discuss the MNIST binary files ingestion.

After that making the corresponding code in Mathematica was easy.

Classification results

Same in Mathematica and R for for SVD and NNMF. (As expected.)

NNMF

NNMF classifiers use the MathematicaForPrediction at GitHub implementations: NonNegativeMatrixFactorization.m and NonNegativeMatrixFactorization.R.

Parallel computations

Both Mathematica and R have relatively simple set-up of parallel computations.

Graphics

It was not very straightforward to come up in R with visualizations for MNIST images. The Mathematica visualization is much more flexible when it comes to plot labeling.

Going further

Comparison with other classifiers

Using Mathematica’s built-in classifiers it was easy to compare the SVD and NNMF classifiers with neural network ones and others. (The SVD and NNMF are much faster to built and they bring comparable precision.)

It would be nice to repeat that in R using one or several of the neural network classifiers provided by Google, Microsoft, H2O, Baidu, etc.

Classifier ensembles

Another possible extension is to use classifier ensembles and Receiver Operation Characteristic (ROC) to create better classifiers. (Both in Mathematica and R.)

Importance of variables

Using classifier agnostic importance of variables procedure we can figure out :

  • which NNMF basis vectors (images) are most important for the classification precision,

  • which image rows or columns are most important for each digit, or similarly

  • which image squares of a, say, 4×4 image grid are most important.