To find all the tags no matter what string of letters or numbers is between them. Basically, what you have to do is use a regular expression: The PC tutorial listed above explains how to do this using Notebook++, which is what I used. To create a list that you could add to a spreadsheet or run through a geocoder, you will need to isolate those tags and, most importantly, what is between them. Everything it identifies as a place name, for example, will now have a tag around it. What the Stanford NER returns to you is a tagged version of your text. Next: getting the place names all into one document

For this step of the process, I recommend the tutorials put together for Rachel Sagner Buurma’s Rise of the Novel class– one for Mac and one by student Claudia Lo for PC. It offers a GUI (graphical user interface) so that you don’t have to do anything resembling programming to use it, although you could use it that way if you wanted to. The Stanford Named Entity Recognizer is a commonly used free tool to extract proper nouns from texts. So, what follows is a how-to based on cobbling together others’ tutorials for each step of the process of taking a relatively clean but unstructured full text and getting it ready to load into a mapping tool. I was also interested in learning how to do this as programmatically as possible.

How does the immigrant subject in early 20th century US narratives plot the world map? I decided to stick with place names, but think about them more broadly. The word frequency experiment with country names got me thinking about more ways to visualize relationship to place.

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