I used the words love and sadness in both of the N-gram tools. The results were fairly the same in each each N-gram because the word sadness was used less substantially than love in both of them . It was interesting to see that in both sadness seemed to have a slight rise in through the 1870 through the 1900 and this could be from an event that happened during this time. A difference between the two is that they seem to have differences in spikes that happened with the word love. They are not cohesive in years. One of the big differences in the graphs are that the amounts projected are drastically different and that could give different reading too. The bookworm N-gram is words per million and the Google N-gram is based on percentages.
Lexos gives the idea that words can be seen and understood in different ways. I chose to show the word cloud because of how i feel about State of the Union speeches and the amount of applause that comes from promises that usually are not fulfilled. The other word clouds were filled with other words like united and liberty as their main words so they were seen to be the most important in those word clouds. This is useful to display the word cloud like this because it provides researchers with an easy visual for them to seen what is most used in certain corpus’s. These word clouds are humanistic because they show patterns within a corpus, but that does that mean they show the full picture. Words clouds lack the deeper understanding of a text that Klein and Drucker ask for when thinking about visualizations.
This type of visualization is intuitive in its aim, but it lacks the proper explanation for what the information is trying to convey. What I believe is the reason behind the graph is to how the books that these authors are influenced by, and or what they have on their books shelves at home for inspiration. The nodes connected to other people that had the same books it seemed, but the reason why is just not there. Yes, it is interesting to see the link between people an the literature that connects them. When reading the paragraphs above the information in the graph becomes much more clear, bit i feel the graphs needs to show its use all by itself to have a good visualization it does not need to have extensive background information surrounding it. It is intuitive because it has the humanistic aspect that visualizations need in the humanities, but it lacks the idea of comprehension that visualizations need.
The data that is being used the artists who have worked with the U.K.’s Tate Art Gallery. The Artists and the countries they are from are presented in the information and it is best suited for the pie chart that is represented here. 80 slices is a large amount of information, but none of the graphs that we have looked at could have made the information look as polished as this. The other ways that Google Fusions makes the information available to researchers is that they proved the information in all different charts. This makes the vantage point of the information different and this way it can be more thoroughly combed through. This tool is exactly what Drucker and Klein are asking for because of the humanistic aspect of the verity of charts. Some of the charts are scientific, but other have interpretative information behind them.