Research Level Classifier

Predict research level from text: http://www.sciencedirect.com/science/article/pii/S1751157713000825

Any use of this code should reference the following article:

Boyack, K. W., Patek, M., Ungar, L. H., Yoon, P., & Klavans, R. (2014). Classification of individual articles from all of science by research level. Journal of Informetrics, 18(1), 1-12. (DOI: 10.1016/j.joi.2013.10.005)

Word-feature weights are contained in the data/word-features.txt.gz file. File is tab-delimited text with 3 columns: research-level, type, word, weight. Where a type of t denotes a title word feature, and a type of a denotes an abstract word feature.

Installing

It may be easiest to run this from a Python virtual environment (https://pypi.python.org/pypi/virtualenv) with python 3.

To install the Python dependencies:

$ pip install -r requirements.txt

This project also comes with an environment.yml file that can be used to create a Conda environment.

Testing

To test, just run the included test script:

$ python test_rlev_model.py
.
----------------------------------------------------------------------
Ran 1 test in 0.247s

OK

Running

Input files should be tab-delimited text files with three columns: id, title, abstract. A sample input file data/sample-data.txt is included. Our output from that input file is also included in data/sample-output.txt.

You should be able to run the classifier like this:

$ python rlev_model.py data/sample-data.txt > data/sample-output.txt

Output is written to stdout and is tab-delimited text with 5 columns: id, prob1, prob2, prob3, prob4. Here prob1 is the probability that the document belongs to research-level 1, and so on.