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

Research Article
Open access

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

Mingyuan Fang 1
  • 1 Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5T 2E4    
  • *corresponding author
ACE Vol.4
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-55-3
ISBN (Online): 978-1-915371-56-0

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 team’s 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.

Keywords:

Classification, TikTok, Principal Component Analysis (PCA), Random Forest.

Fang,M. (2023). Commercial video recognition system for short video (TikTok) based on machine learning. Applied and Computational Engineering,4,159-164.
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References

[1]. Girshick R 2015 Fast r-cnn In Proceedings of the IEEE international conference on computer vision p1440-1448

[2]. Qiu Y et al. 2022 Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training Biomedical Signal Processing and Control 72 103323

[3]. He K et al. 2017 Mask r-cnn Proceedings of the IEEE international conference on computer vision

[4]. Rigatti S J 2017 Random forest Journal of Insurance Medicine 47 31-39

[5]. Biau G and Erwan S 2016 A random forest guided tour Test 25.2 197-227.

[6]. Sshida Y and Sanae K 2018 Fake news and its credibility evaluation by dynamic relational networks: A bottom up approach Procedia Computer Science 126 2228-2237

[7]. Wu F et al. 2020 Research on Self-media Original Video Protection Based on Machine Learning——Based on the original author's perspective Academic Journal of Business & Management 2.4

[8]. Hou Z et al. 2021 Attention-based learning of self-media data for marketing intention detection Engineering Applications of Artificial Intelligence 98 104118

[9]. Li G et al. 2019 Research on Social-media User Classification Based on Machine Learning Modern Library and Information Technology 003.008(2019) 1-9

[10]. Tianchi Commercial Video Data 2020 https://tianchi.aliyun.com/dataset/dataDetail?dataId=53460

Cite this article

Fang,M. (2023). Commercial video recognition system for short video (TikTok) based on machine learning. Applied and Computational Engineering,4,159-164.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-55-3(Print) / 978-1-915371-56-0(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.4
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Girshick R 2015 Fast r-cnn In Proceedings of the IEEE international conference on computer vision p1440-1448

[2]. Qiu Y et al. 2022 Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training Biomedical Signal Processing and Control 72 103323

[3]. He K et al. 2017 Mask r-cnn Proceedings of the IEEE international conference on computer vision

[4]. Rigatti S J 2017 Random forest Journal of Insurance Medicine 47 31-39

[5]. Biau G and Erwan S 2016 A random forest guided tour Test 25.2 197-227.

[6]. Sshida Y and Sanae K 2018 Fake news and its credibility evaluation by dynamic relational networks: A bottom up approach Procedia Computer Science 126 2228-2237

[7]. Wu F et al. 2020 Research on Self-media Original Video Protection Based on Machine Learning——Based on the original author's perspective Academic Journal of Business & Management 2.4

[8]. Hou Z et al. 2021 Attention-based learning of self-media data for marketing intention detection Engineering Applications of Artificial Intelligence 98 104118

[9]. Li G et al. 2019 Research on Social-media User Classification Based on Machine Learning Modern Library and Information Technology 003.008(2019) 1-9

[10]. Tianchi Commercial Video Data 2020 https://tianchi.aliyun.com/dataset/dataDetail?dataId=53460

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