Tags

, , , , , , , , , , , , , , , , , ,

The title of this blog post is not flippant, but is in fact taken from a chapter of a recently-published book, entitled The Hermeneutics of Screwing Around; or What You Do with a Million Books, by Stephen Ramsay. Ramsay discusses how the concept of epistemological order—in his words, the “coherent, authoritative path through what is known”—has evolved over time, and recently been completely transformed entirely by the emergence and insatiable growth of the Internet. The essential premise of our current situation is that there now is far too much information, even on the most specialised subjects, for any one person to take in during his or her lifetime; however, new digital technologies allow us to explore the information in alternative ways.

One of these ways is text analysis. This involves feeding a corpus, or body, of text(s) through a computer programme in order to discover various conclusions that would not be apparent at first glance, or would otherwise have been prohibitively expensive and time-consuming to be carried out by human effort alone. For instance, a basic method of text analysis is counting the frequency of words that appear in a text; the information, which only takes a few seconds to compute, even for a corpus of thousands of words, can be displayed visually in the form of a word cloud. The word cloud currently produced by the RSS feed of this blog (which excludes the older posts) looks like this:

All apparently on-topic for a LIS student!

All apparently on-topic for a LIS student!

This word cloud was produced by Wordle, a widely-known and well-used JavaScript tool that performs this very basic level of text analysis. Wordle in fact refers to itself as a “toy” rather than a “tool”, but it provides a firm foundation for those getting involved in these methods for the first time. Indeed, Julie Meloni refers to it as “the gateway drug to textual analysis” in her blog post on the application’s uses in an education environment. Wordle’s addictiveness is enhanced by the fact that it is customisable at an aesthetic level—the colours, font and basic layout of the cloud can all be changed (hence the colour scheme to match my blog theme).

Wordle, however, is limited in other ways. I can demonstrate this by using the archive of data that I began exporting from Twitter a few weeks ago using TAGS (which has since been growing, as additional Tweets matching the criteria are automatically added every hour). Feeding the corpus consisting of the Tweets (all of which feature the hashtag #citylis, remember) into Wordle produces this word cloud (using exactly the same aesthetics as before):

#citylis wordle

This unwelcome result has occurred because of Worldle’s customisation limitations. Text analysis normally makes use of stopwords—extremely common words such as “the”, “a”, “and” and so forthwhich are not included in the computation. Wordle’s default stoplist does omit these words, but it cannot be edited to suit the needs of each individual word cloud; hence, this one is (as you would expect) dominated by the hashtag “citylis” that links the dataset together, whilst other Twitter jargon such as “RT” (retweet) and “MT” (modified tweet) is also visible.

Fortunately, other, more advanced, tools exist for text analysis. The best of those that I have come across so far is Voyant Tools (initially given the unfortunate name of Voyeur Tools), which is still under development by its creators, Geoffrey Rockwell and Stefan Sinclair, but offers significantly more options. Using the same citylis corpus and removing the unwanted stopwords produces a more meaningful visualisation, which clearly shows the most prolific Tweeters and the most common other hashtags used:

#citylis stopwords removed voyant

Unlike Wordle, the colours, fonts etc. cannot be modified.

Voyant’s tools also extend beyond the word cloud, including a full text reader, a word trends graph, and the ability to locate individual words and see them in context (known as collocation), as can be seen from the screenshot of the entire interface below. This has the potential for more extensive meaningful analysis: for example, looking at the word trends graph, it is clear that there is a spike in activity for the words “lab” and “#bl”—my classmate, Daniel van Strien, explains why here. Another of my classmates, Rachel Giles, has also used Voyant’s ability to customise stopwords to produce more meaningful information about her dataset of cancer-related health journal articles from Altmetric.

There are additional tools not visible in this screenshot, or in development.

There are more tools not visible in this screenshot, or in development.

This is only barely scratching the surface of text analysis, and I will post more on the subject next week as we explore the closely related technique of text mining. What is particularly exciting about text analysis is that it lends itself best to humanities research due to its focus on the written word, but is the complete opposite of the traditional “close-reading” approach due to its use of large datasets. These disciplines have traditionally been exclusively qualitative in nature, and so using what is a quantitative method to analyse the source material has the potential to open up a new wealth of opportunities for research and data visualisation, collectively known as the Digital Humanities. One interesting and freely-accessible example is this The Lord of the Rings project, which has analysed the complete text of the novel (plus J.R.R. Tolkien’s other works in some cases) to produce a range of relevant infographics.

However, I will nevertheless end on a note of caution. Analysing information in this way risks losing the context that underlies it, and visually appealing infographics such as word clouds can be used when they are not necessary at the expense of a more informative presentation, as Jacob Harris demonstrates with a comparison using the same dataset. As with several other aspects of this module, therefore, I would advise that this technique is used to supplement more traditional methods of achieving the same end, not to replace them.

Advertisements