Author Profiling using LDA and Maximum Entropy Notebook for PAN at CLEF 2013

This paper describes the traditional authorship attribution subtask of the PAN/CLEF 2013 workshop. In our attempt to classify the documents based on gender and age of an author, we have applied a traditional approach of topic modeling using Latent Dirichlet Allocation(LDA). We used the content based features like topics and style based features like preposition-frequencies, which act as the efficient markers to demarcate the authorship attributes based on age and gender. We demonstrated tenfold cross validation and observed that our classification approach using Maxent and LDA gave an accuracy of 53.3% for English language and 52% for Spanish Language.