Authorship Attribution via Evolutionary Hybridization of Sentiment Analysis, LIWC, and Topic Modeling Features

Author Attribution is a well-studied topic with deep roots in the field ofStylometry. Less traditional feature sets have not received as much attention.In this paper, we take a deeper look at a few non-traditional feature sets. Weexamine the performance of features derived from Sentiment Analysis, LIWC(Linguistic Inquiry and Word Count), and Topic Models. Using methods fromMultimodal Machine Learning, we combine these different feature sets to in aneffort to improve the performance of Authorship Attribution systems. We then usea feature selection method based on a Steady-State Genetic algorithm known asGEFeS (Genetic & Evolutionary Feature Selection) to examine many differentsubsets of the total feature sets and further improve the performance of theAuthorship Attribution Systems.