Lab 7

N-Grams and Bookworm:

 

I chose to search for the words “song” and “dance” with the Ngram and Bookworm tools. These two graphs show the frequency of both words that are found in literature published through a period of time. The Ngrams graph shows the year 1800-2000 while the Bookworm graph shows 1820-1920. This was the first difference I noticed and the second was that the Y-axis for both tools showed different measurements along the side. The difference between the two words was that the word “dance” wasn’t mentioned nearly as much as “song” was in the Bookworm graph, but the two started to intermingle around 1980 for the Ngram graph. I think the difference between the Y-axis with the two graphs is a major significance because the percentage signs make my head hurt just looking at it instead of a clean cut look like what the Bookworm has. I think readers would much rather look for simplicity than confusing stats. One difference I loved was in Bookworm there is an option to “click for books” that are related to the desired word or words. Ngrams did not have this feature and I think researchers could really use that tool to look for desired books easier and without trouble.

Word Clouds:

Moving onto Lexos, I took this picture of one of the word clouds formed from the uploaded State of the Union corpus file. This shows the frequency of the most used words in the documents and displays them in an eye catching way. This is useful because it allows the researcher to see at a glance what the most used words are in that specific corpus. The advantages are that with one glance you can see the bold faced words and know that that section talks on “constitution” over the other words mentioned. One disadvantage to this is that you can only see a bunch of words jammed together and not the actual sentences they are used in to understand the meaning of the corpus. This visualization opens up new questions concerning data because it leaves the researcher wondering what to really do with the data when it’s outside the original context. Word clouds are indeed “humanistic” graphical displays for they show us trends in specific corpuses, but fail to expose a deeper interpretation of the piece, which the observer would then need to find.

Network Graph:

With this graph, I wasn’t too sure what to make of it. It’s pretty and colorful and anything that resembles jello in physics I’m a fan of, but it was confusing figuring it out at first. In fact, I’m still not 100% sure what I was looking at. I think I figured out that it’s a graph showing the relationship of the characters with each other through nodes. The word “intuitive” for the class means that someone could look at the graph and know what each specific node represents as well as edges and attributes. I’d say it is intuitive though it took me a while to really figure it out. Honestly, the other graphs gave me a headache looking at them and only confused me more. The first graph was better at displaying the information for me. I think my major visualization question concerning the graph was who are these characters and what is their relation to each other since I don’t know them all? Knowing the data provided really would help get the most out of the graph.

Google Fusions:

This next screenshot is of a pie graph using “Tate_artists_percountry” document from our class file. The document is based on information taken from the UK’s Tate Art Gallery. This pie graph shows the percentage of artists that have work in the gallery and the graph shows this by using different colors for different countries. Google Fusions lets researchers visualize data in multiple graphs and not just one, which in turn answers questions that may arise from using other visualization devices. It’s possible we could use Google Fusion in our final project by having a different means of viewing data, meaning these graphs would show us different trends in the same set of data we perhaps didn’t notice before. I thought that the Google Fusion tool was used for a kind of foreground humanistic interpretive purposes because it displays information without any context. It needs an interpretive sense in order to gain the most out of the tool. The visualization is therefore useless without the means to interpret the information not shown.