NLPCC 2017 shared task social media user modeling method summary by DUTIR_923

Abstract

User attribute classification plays an important role in the Internet advertising, public opinion monitoring. While the user points of interest prediction helps the online social media services creating more value. In this paper, aiming at solving the user attributes classification tasks we combine the feature engineering and deep Learning method to reach a higher rank. User attribute classification task is divided into two sub-tasks, in sub-task one, we use the user’s POI (point of interest) check-in history and popular POI location information to predict the next POI that user may visit the future. Sub-task 2 needs to predict the gender of the user. We use the Stacking method to carry out the feature fusion method to complete the feature extraction, based on the output of the logistic regression model then features will be sent to XGBoost model to perform the prediction. In addition, we also used the Convolution neural network model to dig out the user tweets information. Here we replace the conventional Max Pooling method with Attention Pooling in order to minimum the information lost in neural network training. Finally, two methods are given to give a more accurate result.