Supercharge Your Zotero Library Using Paper Machines: Part I

By The GPDH Editors | October 1, 2012

Topic Modeling output for a Zotero collection using Paper Machines

Paper Machines, the add-on that integrates a range of text analysis tools into Zotero, has generated quite a buzz in the short period of time since its release. For those of us that store notes, citation information, PDFs, and article links in huge Zotero libraries, Paper Machines has the potential to be a game-changer in terms of how we visualize our research.

Because Paper Machines is so new, it’s being updated with added functionality every few days. I’ll provide step-by step documentation for how to use specific components of Paper Machines in Part II of this post. For now, I’ll discuss whether or not Paper Machines might be a good fit for your research, the tools that it offers, and how it might help your work.

Paper Machines provides a broad range of text analysis tools, but it’s not meant for everyone’s research. You’ll probably benefit most from Paper Machines if you:

  1.  Already use Zotero to manage your sources. Paper Machines draws on a number of open source tools available elsewhere on the web. If you want to visualize your data but aren’t already comfortable using Zotero, you might want to look elsewhere.
  2. Have a relatively large or robust Zotero library. At the time of this posting, Paper Machines incorporates the full text of Web snapshots and OCR’d PDF files into its text analysis, as well as the title, place, date, and subcollection of a source. The option to include notes, tags, and links to live websites will be available shortly.
  3.  Are collaborating on a Zotero library with a group. Paper Machines is very good at helping you figure out the contents of a collection. If you’re working on a collection with multiple group members, it’s a quick way to visualize what kinds of material your collaborators are adding.

What kinds of analysis tools does Paper Machines employ?

  • A word cloud with the option to filter out commonly used words.
  • Phase nets, which allow you to visualize relationships between common words in your text (for example, x and y; x is y)
  • A Geoparser, which uses location information to produce beautiful visualizations of the places mentioned in your texts.
  • DBpedia Annotation, which produces a visualization of what people, places, and things are mentioned in your texts.
  • MALLET-based topic modeling, which generates visualizations based on commonly occurring topics in your texts. (The author offers some additional information about  information about Paper Machines’ use of topic modeling here.

Read original post here. (Originally published October 1, 2012)