Commercial video recognition system for short video (TikTok) based on machine learning

Abstract

Short video has the features of short duration and high information carrying capacity, which is more in accordance with modern netizens' mobile phone using patterns. With the continual increase of the user scale of smart mobile terminals, many mobile phone users may make full use of the fragmented time to shoot and view short movies. Numerous Internet behemoths are fighting to invest in creating short video platforms since the amount of video user traffic generates enormous commercial prospects. For speeding up the audit teams effectiveness, video classification technology needs to be constantly developed and updated. The article proposed a commercial video detection model with a wide range of data analysis and processing. More specifically, Principal Component Analysis (PCA), feature selection by random forest and discretization using decision trees would be involved in order to transform the original data into features that better express the nature of the problem. The application of these features to Random Forest Model can improve the model prediction accuracy of data. Experimental results demonstrate that the recognition system fulfills outstanding performance. The model achieves 0.90 precision and 0.96 AUC score (area under ROC curve) of excellent evaluation in the corresponding test set.