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.
It may be easiest to run this from a Python virtual environment (https://pypi.python.org/pypi/virtualenv)
To install the Python dependencies:
$ pip install -r requirements.txt
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.
If you see UnicodeDecodeError, you may need to set the --encoding parameter to account for the
input character encoding. For example:
$ python rlev_model.py filename.txt --encoding=utf-8