UCLA

Statistical classification of texts often use dimension-reduction techniques to reduce the number of features in the classification model. However, this often has the consequences of making inputs difficult for humans to decipher. In this thesis, I propose and algorithm using topic modeling as an interpretable dimension-reduction technique for text classification. I apply the algorithm in the context of nationalized campaign rhetoric amongst gubernatorial candidates in U.S. politics, finding such candidates largely speak about issues germane to their jurisdictions.