How many ways are there to visualize a book? Bar chart, scatterplot, word cloud… that’s too narrow thinking. And, yes, there are websites showing how academics visualize text. But what happens out in the wild? Artists? School assignments? Professional designers? Statistics researchers?
Ever so curious, I decided to find out. To come up with some kind of method to search broadly, I picked one book, Lewis Carroll’s Alice’s Adventures in Wonderland and decided to find all the possible visualizations that might pop-up on Google/Bing text search, image search, scholar search. I found more than 40!
On the right are little teeny snapshots of the visualizations that I found. I won’t go into details on all of them, just a few highlights in this article, or you can view the video from the presentation I did for the Lewis Carroll Society of North America (lewiscarroll.org).
If you’re interested in more details, you can read the peer-review research paper. Some of the snapshots are cropped – the links to the full-size images are in the sources at the end of this post.
Visualizations 1-5 are from the visualization research community. Visualization #2 is a word cloud – only one word cloud of Alice in Wonderland is shown here even though hundreds exist. For the purposes of this article, I’m interested in different visualization techniques. Visualization #5 is Brad Paley’s TextArc from two decades ago – an early, wonderful, highly interactive visualization.
6-10 are visualizations from the digital humanities for analyzing text. I like #8, lining up adjectives for a character, providing a sense of the character. In this case Alice’s speech is described as soothing, piteous, or melancholy.
Visualizations 11-18 are from natural language processing. Interestingly, visualizations 15-18 have almost no words – even though they’re about a text.
Visualization #19 is a wonderful visualization from an art thesis by Yi-Chia Cheng. Paragraphs are converted phonetic sounds, shown as symbols using international phonetic alphabet, and stacked into distributions. Distributions can then be created and compared across languages to show how Alice sounds in different languages. (see Cheng’s thesis for many more distributions across languages).
What happens when looking a bit further a field than linguistic research and data analysis?
Visualization #20 is an artistic tool for drawing using sentences from text by Travis Kirton. In this case, an artist has drawn a figure of the caterpillar smoking his hookah using the corresponding sentences from Alice – creating a figurative, non-linear reading of that text.
Visualization #21 is digital micrography – that is – text which has been flowed to fit into arbitrary shapes. Lines of text are curved, bent and sized to follow the predominant flow of the shape. This particular example is from the PhD thesis of Ron Maharik, who automated the technique for even complex shapes such as puzzle pieces for floral shapes, such as this tiny portion from Alice (see figure 10.1, page 68 for the full image).
22-25 are timeline visualizations, some showing changes in Alice’s height over time. 23 includes Freudian analysis in relation to Alice’s height changes, mapping Alice’s psychological development over the course of the book.
Visualization #26 shows only a small portion of a small multiple visualization, showing 20 instances of Alice’s dress from across many publications and movies by Claire Wenzel. Who knew Alice had so many dresses, and an analysis of the fictional representation of Alice’s dresses over time can provide a view on our own changing society.
Visualization 27-28 are interactive physical visualizations, with flaps, tabs and pop-ups.
Visualizations 29-41 are even more broad examples from across the Internet. Some are borderline visualizations, but do use visualization techniques. #29is a list of color-coded places, characters and events. #30 is an infographic providing context to the book as well as content analysis.
#31 is a social network of characters from Alice in Wonderland. Each character is shown with an original illustration from Tenniel. The social network is shown by the lines joining the characters. Along each line is a sentence of text describing the relationship between the characters. Interestingly, this visualization is authored by a costume website — presumably knowing a bit more about the characters and their relationships helps rents more costumes.
#37 is a wonderfully hand-drawn homework assignment, with keywords in heavy marker underlined and rotated as well as lightweight sentences.
#40-41 are unique editions of Alice, with text layout changing, font sizes, caps, etc., modified by the designer in relation to the semantics of the text. Note the call out in #41 overlaying one of Carroll’s logical inversions to form an X.
That’s 41 visualizations. What can be learned from these? In the wild, there’s a lot more text on the visualizations than the research visualizations. And more use of typographic enhancements such as bold, underline, italics and so on.
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These in-the-wild visualizations spurred me to create a number of other visualizations of Alice in Wonderland. Some of these are in my book Visualizing with Text in more detail (Routledge, Amazon, companion site). Large size versions of these images are available in this PDF, CC-license so available to use in teaching, etc. (Also embedded at the end of this post).
Visualization #42 and 43 are sub-word visualizations, indicating properties on syllables.
#44-50 are about words, typically extracted attributes about characters. For example, #49 lists adverbs associated with characters, with font-weight indicating most frequent descriptors – Alice is timid, the Queen is furious, the Hatter is dreadful.
#51- 56 are visualizations of phrases and sentences. #52 shows connections of repeated words from the Mad Tea Party. There’s a huge amount of repetition among the characters, reinforcing their position against Alice.
#55 shows the chapter title and portion of the first sentence for each chapter. Various metrics are shown — the underlying bar indicates the dominant emotion for that chapter as extracted using natural language processing. Chapter 6, Pig and Pepper is highly disgusting; whereas the Chapter 3, A Caucus-Race and a Long Tale is measured as sad.
#57 and 58 are visualizations of the entire book. They could be readable printed out on a poster. #57 has large red text under longer paragraphs. The large red text is a capitalized noun and an uncommon verb, adjective or noun in that paragraph – such as: “Rabbit rabbit-hole”, “Mouse lesson-book”, “Bill roof”, “Duchess frying-pan”, “Queen quarrelling”, and so on. The idea is to form large scale landmarks in the text to easily locate portions of the text. Even larger behind the text are the chapter numbers and titles in yellow.
#58 is a version of the entire text of Alice where the text is increased in size if it has been quoted on the Internet. After collecting and processing 200 quotations, the most famous quotes from Alice stand-out larger than the surrounding text. You can immediately see the most quotable quotes, and step closer to read the surrounding text. Interested in what’s the largest text?
- “Who in the world am I? Ah, that’s the great puzzle!” (Alice, Chapter 2)
- “We’re all mad here.” (Cheshire cat, Chapter 6)
Sometimes it’s important to think outside of the box of word clouds and bar charts: there is so much more possible and feasible.
Yes, there are more, so I see from responses on Twitter and elsewhere. 59-61 are some NLP visualizations: 59 creates little squares, one per sentence, brightness by sentence length. 60 transforms words to a vector space and plots, 61 isn’t quite a word cloud. 62-64 are more artistically driven: 62 animating sentences, 63 punctuation only, 64 is words inside large words which in turn forms a rabbit. I would not have though one could quite manage to get the layout of words to clearly form letters of larger words – apparently it’s quite feasible.
A Wonderland of Data Visualization
I did a presentation for the Lewis Carroll Society of North America (LCSNA) titled “A Wonderland of Data Visualization.” This presentation is more accessible to a wider audience and should be available on Youtube under LCSNA channel.
LCSNA is aware of additional visualizations: 65 is a set of interconnected bar charts comparing content from the original Under Ground vs. Alice’s Adventures in Wonderland – note that the first 5 chapters are largely the same, the latter chapters are largely new content. 66 is a similar analysis presented as a table, which is a type of visualization. 67 is another variation on a timeline indicating Alice’s height chart, in this example using Tenniel’s original illustrations and a related table. 68 is also a timeline in the wild, in this example, a highly illustrated timeline with short captions.
- Davies, J. Word Tree [with Alice in Wonderland]. https://www.jasondavies.com/wordtree/?source=alice-in-wonderland.txt&prefix=dear (original WordTree by Wattenberg M, Viégas FB. The
word tree, an interactive visual concordance. IEEE transactions on visualization and computer graphics. 2008 Oct 24;14(6):1221-8.)
- Wolfram. Word Cloud examples [using Alice in Wonderland]. www.wolfram.com/language/11/new-visualization-domains/oriented-word-clouds.html. (original Milgram, S. and D. Jodelet. “Psychological maps of Paris”, Environmental Psychology, 1976,)
- Semantic Knowledge. Gephi GEXF Exports in Tropes. https://www.semantic-knowledge.com/doc/V81/text-analysis/gephi-gexf-exports.htm (created using Gephi, Bastian, M.; Heymann, S.; Jacomy, M.. “Gephi: An Open Source Software for Exploring and Manipulating Networks.” International AAAI Conference on Web and Social Media, North America, 2009)
- Tanahashi, Yuzuru, and Kwan-Liu Ma. “Design considerations for optimizing storyline visualizations.” IEEE Transactions on Visualization and Computer Graphics 18.12 (2012): 2679-2688.
- Paley WB. TextArc: Showing word frequency and distribution in text. Poster at IEEE Symposium on Information Visualization. 2002.
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