CAPS-PRC: A System for Personality Recognition in Programming Code

This paper describes the participation of the CAPS-PRC system developed at the LMU Munich in the personality recognition shared task (PR-SOCO) organized by PAN at the FIRE16 Conference. The machine learning system uses the output of a Java code analyzer to investigate the structure of a given program, its length, its average variable length and also it takes into account the comments a given programmer wrote. The comments are analyzed by language independent stylometric features, including TF-IDF distribution, average word length, type/token ration and more. The system was evaluated using Root Mean Squared Error (RMSE) and Pearson Product-Moment Correlation (PC). The best run exhibited the following results: Neuroticism (RMSE 10.42, PC 0.04), Extroversion (RMSE 8.96, PC 0.16), Openness (RMSE 7.54, PC 0.1), Agreeableness (RMSE 9.16, PC 0.04), Conscientiousness (RMSE 8.61, PC 0.07).