  Classification and topic modeling are popular techniques in machine learning
that extract information from large-scale datasets. By incorporating a priori
information such as labels or important features, methods have been developed
to perform classification and topic modeling tasks; however, most methods that
can perform both do not allow for guidance of the topics or features. In this
paper, we propose a method, namely Guided Semi-Supervised Non-negative Matrix
Factorization (GSSNMF), that performs both classification and topic modeling by
incorporating supervision from both pre-assigned document class labels and
user-designed seed words. We test the performance of this method through its
application to legal documents provided by the California Innocence Project, a
nonprofit that works to free innocent convicted persons and reform the justice
system. The results show that our proposed method improves both classification
accuracy and topic coherence in comparison to past methods like Semi-Supervised
Non-negative Matrix Factorization (SSNMF) and Guided Non-negative Matrix
Factorization (Guided NMF).
