Today in class I briefly mentioned TF-IDF (Term Frequency-Inverse Document Frequency) as a possible way for us to identify "give away" words that might appear more frequently in a particular document. Here are some introductory explanations of the method:
- A tutorial on calculating TF-IDF scores in Python using the TextBlob module
- A more mathematical explanation of the method from this book on information retrieval
- A discussion of how TF-IDF can be used to gauge document similarity.
I also mentioned Named Entity Recognition in class; this is the same library used by the Rezo Viz tool that Daniel and Alyssa showed us in their Voyant Tools presentation. It may be possible for us simply to use Voyant as an interface for NER and export a list of place and person names from our ads, but we need to look into this further.