Learning how to use Gale Digital Scholar Lab this week made me think about how I could use text analysis tools in not just my project, but other aspects of humanities research as well. I really liked how the platform walks you through the entire process, from collecting the data, to cleaning and visualizing it. Even if you don’t have technical experience with Gale Digital Scholar Lab, or tools similar to it, it still manages to feel approachable, which makes it a good entry point for someone trying to learn more about the “digital” aspect of digital humanities. One of my favorite things about Gale Digital Scholar Lab is the fact that you’re able to pull materials directly from the primary source archives, and then immediately begin working with them. The most interesting part for me was experimenting with all of the different analysis tools, such as topic modeling or sentiment analysis, and seeing how they could reveal connections that wouldn’t have been visible by just parsing through the text manually. The thing I found the most frustrating was definitely the data cleaning, which felt pretty time-consuming and rather confusing when deciding which filters to apply.
I think I would be pretty comfortable helping someone else learn the basics of Gale Digital Scholar Lab, especially since the process was pretty intuitive once I started to understand the workflow. Looking through the student research projects was really encouraging for me, because it showed me that meaningful analysis doesn’t require a super substantial dataset, or super advanced skills. It also made me more confident about using Gale Digital Scholar Lab in my own research, as the bulk of my digital project (right now) consists of me looking at a wide array of articles, and parsing through a lot of text. Going forward, I would really like to explore more of Gale’s visualization features, and see how I could combine it with other platforms like Voyant or ArcGIS StoryMaps.