LibKey Nomad
Getting to the Plain Text
Typically, your goal should be to obtain or create a Plain Text (UTF 8) version of the work(s) you will be analyzing. This may involve:
- Finding a copy that is already in Plain Text
- Finding a digital copy and convert it (if necessary) to Plain Text
- Scanning and then extracting the text (using Optical Character Recognition) from a physical text
Optical Character Recognition (OCR):
Many free tools exist, but pay attention to their privacy and security practices before giving them your texts. Here are a few that function well with Carleton infrastructure.
- Adobe Acrobat - Edit Scanned PDFsAdobe's documentation on creating editable PDFs.
- Google Drive - Convert PDFs and Images to TextGoogle's instructions on converting PDFs and Images to Text files.
- OneNote - Copy Text from PictureMicrosoft's instructions on how to copy text from a picture.
Converting File Types
- Calibre: Ebook ManagementPowerful free tool for reading and working with ebooks. Note their documentation on converting ebooks to plain text.
Clean & Manipulate the Text
Most plain text will need some cleaning and manipulation before it's useful for analysis. This can also be an iterative process as you move through your project. Here are common reasons for cleaning and manipulation:
- Remove extraneous text (license agreements, recurring text that was added to indicate what page of the source document is reproduced here, etc)
- Normalize spellings across the document/collection
- Add unique strings of characters to the text to mark times when features of the text appear that you want to analyze (i.e. add "#character-1" every time the first of a few unique and identifiable but unnamed characters are referenced in a text)
Common Tools for Cleaning Text
- BBEdit
This link opens in a new window
Even the free version of this software allows for excellent plain text manipulations, including find/replace across multiple files. - OpenRefine
- This link opens in a new window
This is a powerful tool for working with data (text or otherwise). You can clean and normalize messy data, transforming it from one format into another, and much more. Be sure to take a look at the three short tutorial videos on the main page before beginning your work.
Scripting and More
You may find that the options here are not powerful enough for you. For example, maybe you want remove a list of language- or domain-specific Stop Words from your texts. Your librarian can help you identify lists of stop words, and Paula Lackie (plackie@carleton.edu) or the Data Squad for data-manipulation support.
