Multi-lingual author profiling using stylistic features

Author profiling is the identification of an author’s traits by examining the text written by an author. Author profiling has many useful applications in security, criminal, marketing, identification of false accounts on shared communication websites, etc. We submitted our system to the FIRE'18-MAPonSMS (Multi-lingual Author Profiling on SMS), a shared task to classify the attributes of an author like gender and age group from multilingual text specifically English +Roman Urdu. Roman Urdu is common language specifically in SMS messaging, Facebook posts/comments and chats blog of games etc. Our presented system is based on 29 different stylistic features. On the training dataset, we have achieved best Accuracy = 73.714, for gender while using all 14-language-inde-pendent features together and Accuracy = 58.571 for age group by using all 29 features together. We obtained Accuracy = 0.55 and 0.37 on testing data for both gender and age respectively.