This post is the fourth in a set of 5 written by the Digital History Fellows at the Roy Rosenzweig Center for History and New Media. The original post by Jannelle Legg can be found on the Digital History Fellowship Blog.

As described in previous posts, the first year Digital Fellows at CHNM have been working on a project under the Research division that involves collecting, cleaning, and analyzing data from a corpus of THATCamp content. Having overcome the hurdles of writing some python script and using MySQL to grab content from tables in the backend of a Wordpress install, we moved on to the relatively straightforward process of running our stripped text files through MALLET.

As we opened the MALLET output files, excited to see the topic models it produced, we were confronted with a problem we didn’t reasonably anticipate and this turned into a rather important discussion about data and meaning.

As a bit of background: topic modeling involves a process of filtering “stopwords” from a data set. Frequently a list of stopwords includes function words, or terms that appear repeatedly in discourse, like: “a, an, the”. These are filtered out because they serve a grammatical purpose but have little lexical meaning. Additionally, errors, misspellings, and lines of code that were skipped in the previous steps can also be filtered out at this stage.

As we opened the file of keys produced by MALLET, we found that some terms appeared that raised questions about what should or should not be included in our analysis. In particular, the discussion centered around spelling errors and function words in Spanish and French.

The conversation that followed, reproduced below, was significant and as people look through the results of this project or consider their own efforts reproduce something like this elsewhere, we’d like to be transparent about the decisions we made and, perhaps, spur a discussion about how to address scenarios like this in the future.

I’m excited to see this once it’s all graphed.

Hmm, that could be an organization…

There was a camp there.

This adds a larger question: do we remove misspellings?

For clarity?

We don’t want to skew the results.

Some of it occurred when we stripped all the alphanumeric stuff out.

It took out apostrophes- causing words like “I’ve” to become “I ve”.

That the content is generated spontaneously

lends itself to deviations from appropriate spelling ect.

Look at 17 and 18.

Its possible to strip out the camps that are not in English

or even to run analysis on them separately.

I don’t want to skew the results but this also throws things off.

As outlined above, when we opened the text file with keys, new questions were raised about the relevance and complications of running a particular stoplist on a corpus of texts. Similarly, we were forced to rethink how we handle misspellings and unfamiliar abbreviations. In the end, we tracked down stoplists for Spanish (and French) terms, so that no function words in any language would skew the results of our analysis. We also carefully examined the keys to identify abbreviations and misspellings and decided that they are a significant contribution to the analysis.

A few questions remained for us: how might removing non-alphanumeric characters (a-z,A-Z,0-9) alter the meaning of special characters used in languages other than English? How have others responded to spelling errors? How significant are errors?

Hopefully a post of this nature will foster discussion and produce a stronger, more complete analysis on this and other documents.