It's from a University of Waikato researcher so they are likely using Weka to do all the "textbook" transformations before the final analysis.[1] You could als

It's from a University of Waikato researcher so they are likely using Weka to do all the "textbook" transformations before the final analysis.[1] You could also go a long way towards reproducing the results with Python by installing NLTK and sci-kit learn. NLTK for tokenizing, stemming and similarity metrics.[2] Scikit-learn for tf-idf vectorization, k-means and even some of the datasets like 20news.[3] I use NLTK and scikit-learn for http://jkl.io http://jkl.io Weka has a partially free book and NLTK has a free book for you to look into as well. Scikit-learn has some of the best documentation and examples I've seen. But I agree that more papers should come with working implementations. The problem is that academics are judged on their publications and publications frequently build on previous ones, so the work for this paper might be the starting point for two more papers the author is planning and they don't want someone to just come along and publish those logical extensions off their hard work as they want the credit for themselves. It will be hard to change this kind of academic culture until getting your dataset or code used and cited becomes more prestigious in metrics used for academic promotions. [1]http://www.cs.waikato.ac.nz/ml/weka/ http://www.cs.waikato.ac.nz/ml/weka/ [2]http://www.nltk.org/ http://www.nltk.org/ [3]http://scikit-learn.org/stable/ http://scikit-learn.org/stable/