For the second step in this lab, I chose to compare the words “love” and “marriage” over time. The first image is the graph from Google ngrams.

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In this graph, it’s clear that love is talked about much more frequently in novels than marriage is, which is interesting considering the time period. Usually, love and marriage would go hand in hand during the times of these publications, but love is obviously much more frequent which shows a difference between the two ideals. Additionally, love’s frequency lowers in the as the years progress, yet marriage stays relatively constant through the years. The gap between love and marriage is distant, but not so distant that there doesn’t appear to be any relationship.

The second graph is from Ben Schmidt’s word frequency tool, Bookworms, and is below.

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Here, the difference between love and marriage is much bigger than the gap in the first graph. While this graph shows a slight rise in marriage towards the end of the mapping, the word is as similarly consistent as the first graph. One major difference is that in the first graph, love shows a much steeper decline than it does in the second.

Ultimately, these graphs are similar but different because of the tools and their respective intricacies. The bookworm tool is much more in-depth than Google ngrams since you can customize languages, regions, subjects, and much more in order to get a graph or a comparison that you are aiming for. The differences between graphs, though relatively small, are important when looking at the overreaching idea of using this type of tool for data visualization. Humanists researching in this manner can use either tool, but the variations between the two show why these methods are still considered experimental in nature.

This is the word cloud of the states of the union topics.

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This is a visualization of all of the topics labeled as “0” in the state of the union corpus as found in the complete list of topics that I downloaded from the box drive and uploaded into Lexos. The main advantage of this visualization is that it’s both easy to see and easy to use. If you’re looking for a quick glimpse or a general overview into a corpus or a single topic, this is an easy way to do it. Even though there is no additional data here, you can garner a good deal of information and understanding just by looking at the most used words (the biggest words) in the cloud. Clearly, congress, states, and a united front were very important during the states of the union. I think this image is a great place to start for research and definitely opens up the topic for further discussion or investigation.

For the next step I played around with the network graph from This graph is interesting because it is interactive and also supplies the user with a lot of information via connections. While solely looking at connections between works or authors tells you a little bit, it doesn’t tell you everything, which is why this method would also best be combined with another more invasive method of analysis. For example, this graph tells me that Alice Waters has a cookbook and a wine drinking book in common with her work, which leads me to believe that she is probably a gastronomical writer of some sort. However, this graph does not tell me to what extent her writing is food-related or anything further than surface level analysis, which is why a second method would make this much more effective as a tool.

Finally, I used Google Figures to create a pie chart that graphs the birth places of all of the artists in the Tate Museum. The screenshot is below.

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This pie chart is split into very tiny increments (80 slices), so it manages to get a lot of information in a small visualization, something that many of the other methods do not accomplish as well. The topic that is up for analysis is a unique one because it brings up an idea that most people don’t think of during a museum: where each artist was born. Because the majority of the artists features in the Tate Museum are British or American, researchers would have a better chance at pinpointing the types of art that are most likely to be found in the museum along with the approximate dates of the majority of the pieces; since Americans account for  little over 11% of the data, there is a significant portion of history that could be missing (the time before American art was curated and preserved).

Google Fusions is a great tool for data visualization since it lets you customize the visual in any way you prefer to fit your most specific needs. Using fusions would definitely be useful for our final project since it offers a plethora of visualizations to choose from, and since we are collaborating as one large class, it would be cool to see the same data presented from different vantage points in the same way or the same data presented from the same vantage points in different methods. Unlike many of the other tools, this method is the one that stands alone the most. Even though a researcher could not get all of the necessary information for study from a simple pie graph, it’s a more comprehensive method than the others aforementioned. However, like the other methods, I think Google Fusions would be most affective when combined with another form of data analysis.