11 Matching Annotations
  1. Nov 2023
  2. Aug 2022
    1. For the sake of simplicity, go to Graph Analysis Settings and disable everything but Co-Citations, Jaccard, Adamic Adar, and Label Propogation. I won't spend my time explaining each because you can find those in the net, but these are essentially algorithms that find connections for you. Co-Citations, for example, uses second order links or links of links, which could generate ideas or help you create indexes. It essentially automates looking through the backlinks and local graphs as it generates possible relations for you.
  3. Jul 2022
    1. Adversely, the Topics feature did not seem super helpful which was surprising because I initially thought that this feature would be helpful, but it just did not seem super relevant or accurate. Maybe this is because as a work of literature, the themes of the play are much more symbolic and figurative than the literal words that the play uses. Perhaps this function would work better for text that is more nonfiction based, or at least more literal. 

      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.

  4. Mar 2022
  5. Feb 2022
  6. May 2021
  7. Mar 2021
  8. Feb 2021
    1. Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data. A lot of Apps are available for various kinds of problem domains, including bioinformatics, social network analysis, and semantic web.
  9. Aug 2020
  10. May 2019
    1. Methodology The classic OSINT methodology you will find everywhere is strait-forward: Define requirements: What are you looking for? Retrieve data Analyze the information gathered Pivoting & Reporting: Either define new requirements by pivoting on data just gathered or end the investigation and write the report.

      Etienne's blog! Amazing resource for OSINT; particularly focused on technical attacks.

  11. May 2018