I just published a Python package called “EasyText” that was funded as part of a UCSB undergraduate instructional development grant with John W. Mohr. The project came about as a follow-up of Dr. Mohr’s Introduction to Computational Sociology course I helped with in Spring 2016 (more about that). This project was created with the goal of bringing a broad range of text analysis tools into a single interface, particularly one that can be run from the command line or using a minimal amount of Python code. Try it out using pip: “pip install easytext” (see PyPi page).
The command line interface is particularly focused on generating spreadsheets that students can then view and manipulate in a spreadsheet program like Excel or LibreOffice. Students can perform interpretive analysis by going between EasyText output spreadsheets and the original texts, or feed the output into a quantitative analysis program like R or Stata. The program supports features for simple word counting, noun phrase detection, Named Entity Recognition, noun-verb pair detection, entity-verb detection, prepositional phrase extraction, basic sentiment analysis, topic modeling, and the GloVe word embedding algorithm.