The question

My question was whether this visualization method could provide insight into who wrote the disputed essays. Apparently Hamilton claimed quite a few more essays, that later Madison claimed that he wrote. Lin Manuel Miranda’s lyrics are consistent with conservative 51 essays for Hamilton. Of course there is a lot of fantastic work already done on this, but I just ignore this fact for the sake of the exercise. The federalist dataset in the corpus package provides authors if known; but disputed authorship is designated NA.

table(federalist$author, useNA = "ifany")

## 
## Hamilton      Jay  Madison     <NA> 
##       51        5       14       15

In fact, in the end, I’ll present two network visualizations, using different preparations of the data. For the first, I use the text preprocessing methods that I’ve previously used with this vis technique, which, I think is appropriate for thematically associating documents. For the second, I reduce the amount of text preprocessing, which might in better who-wrote-it, “textual forensics” work. This mirrors how this mini-project developed, and I think two approaches are interesting to contrast.

Getting started

One thing that I like about this four-hour-and-counting project (my one-hour one-off dreams elude me again), is that it makes use of so many of my favorite packages. Let’s load them.

library(quanteda)  # text analysis
library(tidyverse) # ggplot for graphing and dplyr for wrangling
library(tidygraph)  # for relational data manipulation
library(igraph)    # network graph tools
library(ggraph)   # the link between ggplot and 
library(viridis)   # for better color palette for points on map
library(plotly)  # plan to use this in the future, for mouse-over and view full text. 

Now we prepare the corpus. We use a bag-of-words approach, where the tokens of each document are counted. This is done using the document feature matrix function or dfm(). I set several preprocessing options to “true”; I think these represent pretty typical preprocessing choices.

Using the preprocessed data, we calculate a similarity score. I use the popular cosine similarity in this exercise. The procedure looks at the “direction” of the vector of a document created with each token as a dimension, and compares this to that calculated for other documents. A similarity scores between 0 (low similarity) and 1 (identical token ratios) results for each document pair. Here is the code, which uses functions from the quanteda package:

corpus <- corpus(federalist)

dfm <- dfm(corpus, 
            remove_numbers = T,
             remove_punct = T,
              remove_separators = T,
           stem = T,
            remove = stopwords("english")
           )

# Calculate cosine similarity
similarity <- 
  textstat_simil(dfm, 
                 method = "cosine",
                 upper = T, diag = T)

## Warning: Arguments upper, diag not used.

Resulting Graph

Now the resulting similarity matrix, and treat it as the basis for my network matrix. I simply need to have ones-and-zeros instead of the similarity measure (which takes values between 0 and 1).

There are different procedures that I could apply. What I think works well and makes sense for corpus visualizations is to draw an edge (1) when, for a pair of documents, the cosign similarity for the pair is within the top quantile (say .95) of the set of document similarities for either pair member; otherwise don’t draw an edge (0). My rule here is that a connection should be drawn to the top two most similar documents from any document.

In this blogpost I’m not going to echo all the code. It is a bit lengthy; it could also stand to be cleaned up a bit. (I haven’t yet implemented the power of the tidygraph package - which I’ve tried elsewhere; it is all that you would hope for!) Perhaps, it is that want I like to keep a few implementation secrets to myself, at least at this point.

So I just give you the resulting graph:

## Warning: Ignoring unknown parameters: colour

Take 2: A revised preparation

Now I do the same thing using the alternate preparation. When I used the above preprocessing method, basically the template that I’d used before, I felt disappointed. I expected there to be more “clustering” among the authors; but what seemed to really dominate was topics. It made sense, but I still felt let down.

Thankfully, I took a step back from fiddling with the graphing parameters to review all the code. I came back to the preprocessing code. Hmm. What was being done to alter the raw text? Lots! And there could be the idiosyncratic, tell-tale authorship “signatures” in what was being removed and standardized. So, I modified the preprocessing step to do less to the raw text; below I just comment out the options so the difference is clear. Instead of removing stopwords, we keep them. And we don’t do any stemming. We keep punctuation and numbers too. Then I prep the recalculated similarity scores into an adjacency matrix to allow for a revised network graph.

corpus <- corpus(federalist)

dfm <- dfm(corpus, 
           # remove_numbers = T,
            # remove_punct = T,
             remove_separators = T #,
            # stem = T,
            # remove = stopwords("english")
           )

# Calculate cosine similarity
similarity <- 
  textstat_simil(dfm, 
                 method = "cosine",
                 upper = T, diag = T)

## Warning: Arguments upper, diag not used.

## Warning in summary_character(texts(object), n = n, tolower = tolower, ...):
## verbose argument is defunct

## Warning: Ignoring unknown parameters: colour

Wrapping up

## Joining, by = c("name", "title", "venue", "date", "author")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Arguments upper, diag not used.