Alien is my favorite movie.
Ridley Scott’s 1979 masterpiece of cinema hooked me immediately when I first watched it at maybe 10-years-old. The suspense! The action! The badassery! The acid! It had everything a kid could want, and then some.
But I came to appreciate it in new ways as both my view-count of Alien and my age grew. The “badass-ness” of Ripley as a person, a woman, and an alien-kicking survivalist took on new textures and inspiration. The creepiness of MU/TH/UR, the ship’s operating system, deepened, echoing that of HAL 9000, cautioning against unfettered technocratic capitalism.
Is this, perhaps, the genesis of my suspicion of the belief that “technology will save us”? Does it all come down to the long standing joke about therapy, do I just have MU/TH/UR issues?
While unpacking these questions is outside the scope of this post, dear reader, the exploration of Alien is not.
I, like Ripley, have decided to explore realms previously unknown to me.
I, unlike Ripley, have done so from the comfort of my living room with my dog snoozing by my feet.
Voyant Text Analysis
Ripley had Nostromo, and I have Voyant, a text analysis tool that comes highly recommended by digital humanists. Since my experience with text analysis more-or-less begins with “CTRL+F” and “OCR”, then caps out with manual qualitative interview coding, the extremely user friendly interface of Voyant seemed a solid place to begin.
Two things quickly became apparent.
One, I really like the “Contexts” visualization tool.
It is both very useful to the work of historians (context matters!) and it seems a good tool to use in order to address the concerns of text analysis and text visualization raised by leaders in the field of digital history and digital humanities.
What are the contexts of the terms? How are they being used? What phrases are they part of? I considered these questions while clicking through the “Contexts” of Alien and, if I were conducting research, this application of text analysis would be helpful in identifying data issues and how to address them.
Two, text (visualized and quantified) do not always tell the truth.
Catherine D’Ignazio and Lauren Klein critiques of the neo-liberal belief that “numbers speak for themselves” really came into focus. I, like many people, learn through “hands-on” exploration. Here, hands-on is in quotes because it is much more conceptual for digital history and digital humanities tools. But, digital or analog, a tool is a tool and it can only be learned by using it.
Now, unlike the quantitative research that is the subject of D’Ignazio and Klein’s work, there are no stakes for the practice Voyant analysis I did with the Alien screenplay. But it still instructed me in the importance of considering how the type of document could skew the analysis, context of its creation, and words or terms that are (or strategically are not) found in the document.
For example, I added “int” to the “stop words” list for this practice analysis. Because a screenplay is the source of the text, the term “int” appears a lot. Int, in this context, is a short-hand term for “interior” (since Alien is set in a spaceship, “int” is use A LOT).
Removing “int” from the analysis changed the visualizations. The top terms adjusted, the contexts of those terms shifted slightly, and the metrics for usage changed. To those who are OG text analyzers, this is probably a “no, duh” comment. But to this newbie, I found it interesting how removing one word from analysis quickly changed what I saw in the text.
Visualizing a visual document
With these a ha moments in mind, I continued to play with Voyant. An aspect of this tool that could prove to be very helpful in finding “jumping off” points in documents is its ability to focus in on one (or more) selected terms. Below is a recording of how Voyant helped me visualize the word “melt” and its usage in the screenplay. By searching for just that term in the usage graph, I could then also see the context of its use as well as exactly where in the document it is used. This is certainly an aspect of text analysis that I can envision using in research.
I applied this further by visualizing the term “Mother”, the name Ripley and her colleagues used to “talk to” the operating system of Nostromo, MU/TH/UR. Surprisingly (at least to me), the term “Mother” is not used in the script as often as I would have thought, because MU/TH/UR is always there and essentially a member of the crew. But, again, this drove another lesson home about the use of text analysis- digital historians and digital humanists who utilize this tool to generate data for their work must go through great lengths to consider inferences, euphemisms, and other uses of language that may skew the quantitative results.