It's possible that the process of note taking and excerpting may impose levels of analysis and synthesis on their users such that when writing and synthesizing their works that they more subtly expect their readers to do the same thing when their audiences may require more handholding and explanation.

Here, both the authors' experiences and that of the cultures in which they're writing will determine the relationship.

There's lots of analogies between thinking and digesting (rumination, consumption, etc), in reading and understanding contexts.

Source: https://hypothes.is/a/hhCGsljeEe2QlccJUQ55fA

things to do to understand text:

• visualize single texts
• measure features of text
• identify distinctive words
• find or organize works
• model literay forms
• model social boundaries
• unsupervised modeling

I read your Voyant analysis of Henrik Ibsen's "A Doll's House," and I think we almost pick the same tools that we believe to be crucial for our text analysis. Like you, I mostly visualize my chosen literary work with Cirrus, Terms, Berry, and Trends. I also use links to ?look into how these words are used interdependently to contextualize the story told. I also had difficulty understanding how functions like Topics would benefit my understanding of the texts on a layered and complex level. I checked and thought maybe the problem was with the word count of the document. By default setting, Topics generates the first 1000 words in a document, and A Doll's House has 26210 words. In order to use this tool in the most efficient way possible, you can try to use the Topics slider ( the scroll bar) to adjust the number of topics you want to generate (max is 200). I have read A Doll's House before, so I couldn't speak for those who haven't. However, the clusters of chosen terms hint to me that this fiction deals with bureaucracy and finance via repeated words like "works," "money," and "paper." I can also recognize some words classified as names, so many characters are involved in the story. There is also a vague clue of the story's setting, which is during the winter season, from the repetition of the word "Christmas." It appears that someone is getting angry at someone for their wrongdoings, and this drama occurs in a family. While Topics cannot give me a complete storyline, it gives me a good chunk of puzzles to piece together the core gist of the story. It happened to me when I analyzed Herman Melville's Moby Dick. Words like "whale," "sea," "sailor," and "chase" allowed me to make a reasonable assumption that there was a group of sailors that went after a giant whale in the sea. I still prefer to use other tools, but that was how I utilized Topics for my knowledge of the text. I agree that text with more literal content, like self-help books, would definitely yield better results with Voyant Tools' Topics.

Grimmer & Stewart (2013) - Text as Data: The Promise and Pitfalls of Automatic ContentAnalysis Methods for Political Texts

• urn:x-pdf:5bb906b65a58358ba98fbc3ee72836f9
• https://is.gd/rBVTEI

Nelson (2020) - Computational Grounded Theory: A Methodological Framework

• urn:x-pdf:9f22952783af4581a3525c1a1497328a
• https://is.gd/k2YLcm

I don't entirely agree with this part of the statement because the digital tools we have allow us to both view information in an entirely new way and to see connections that we couldn't have seen very readily. For example, the ability to take any written work and create a concordance of words can give us great insight that just reading the work would not have. If we wanted to see to what degree society is viewed from a male vs. female perspective between 1920 and 2020 we could analyze specific words in several pieces of literature from those time periods to see how significantly each gender is represented. If not impossible to do before digital tools, it would certainly be so laborious as to render it an insignificant goal in the scheme of humanistic inquiry. Thus we there is a basis for discovery within digital tools.

Xu, Z., & Guo, H. (2018). Using Text Mining to Compare Online Pro- and Anti-Vaccine Headlines: Word Usage, Sentiments, and Online Popularity. Communication Studies, 69(1), 103–122. https://doi.org/10.1080/10510974.2017.1414068

Krupenkin, Masha, Kai Zhu, Dylan Walker, and David M. Rothschild. ‘If a Tree Falls in the Forest: COVID-19, Media Choices, and Presidential Agenda Setting’. SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, 22 September 2020. https://doi.org/10.2139/ssrn.3697069.

Arqoub, O. A., Elega, A. A., Özad, B. E., Dwikat, H., & Oloyede, F. A. (2020). Mapping the Scholarship of Fake News Research: A Systematic Review. Journalism Practice, 0(0), 1–31. https://doi.org/10.1080/17512786.2020.1805791

Bail, C. A. (2016). Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media. Proceedings of the National Academy of Sciences, 113(42), 11823–11828. https://doi.org/10.1073/pnas.1607151113

Samuel, J.; Ali, G.G.M.N.; Rahman, M.M.; Esawi, E.; Samuel, Y. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. Preprints 2020, 2020050015 (doi: 10.20944/preprints202005.0015.v1)

Golino, H., Christensen, A. P., Moulder, R. G., Kim, S., & Boker, S. M. (2020, April 14). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right-wing and left-wing trolls in the 2016 US elections. https://doi.org/10.31234/osf.io/tfs7c

Kennedy, B., Atari, M., Davani, A. M., Hoover, J., Omrani, A., Graham, J., & Dehghani, M. (2020, May 7). Moral Concerns are Differentially Observable in Language. https://doi.org/10.31234/osf.io/uqmty

This text analysis that it contains words written in hebrew and deciphering of the first sentence of the text using hebrew translation seems to align with what this author is saying about the text being passed down through the family.

She made recommendations to the priest, man of the house and me and people.

[Source] (https://hyp.is/GB7sZKjvEeidoGeGo8L6jA/www.independent.co.uk/news/science/mysterious-manuscript-decoded-computer-scientists-ai-a8180951.html)

This seems to align with the analysis of Sukhwant Singh

Sukhwant Singh's analysis here seems to fit with a lot of other's partial analysis/observations such as multiple characters representing the same character, certain characters only appearing at the end of words etc. It seems quite compelling. The dates however, are a century too early although that does not necessarily dispel his theory that it is written in Landa Khojki.

This might suggest the same thing as Tiltman's analysis in that a single character may take several forms.

When learning that there were over 100 characters used in the manuscript my first thought was that perhaps variations of a character were used to represent the same character.

This is very interesting. It seems to suggest that each word may be scrambled based on the characters used.

Here is a documentary on the Voynich Manuscript published in 2014.