Author(s)

Yimeng Ren ¹, Bo Xiao ²

Affiliation(s)

¹ School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China
² School of Business, Ningbo University, Ningbo, 315211, China

Corresponding Author

Yimeng Ren

ABSTRACT

With the rapid development of today's society, the widespread use of the Internet and the rapid development of network information technology, people are taking the initiative to accept and use the Internet. Compared with general network goods, short videos have a higher degree of dissemination as well as better acceptance. In the development of the Internet industry, short video content has been enriched, driving the growth of user scale and viscosity, and becoming a major source of incremental mobile Internet hours and traffic. At the same time, viewers also prefer to post their personal feelings to the response comment section when watching, thus interacting and communicating with other people watching, thus generating a large amount of comment information. These comments intuitively express the user's likes and dislikes and demands, and the number of comments also reflects the popularity of the video to a certain extent. Predicting the number of comments on a short video allows short video operation platforms to understand and master the popularity of the video, increase traffic investment in potential short videos in a targeted manner, and help potential short videos to gain higher popularity and create a more appealing short video ecological environment. In this context, this design uses data analysis tools to analyse and predict the number of short video comments. In this paper, three models, XGBoost, Random Forest model and LASSO, are used to analyse and predict the number of short video comments, and by comparing the MSE, interpreting the SHAP graph, the model with the strongest prediction ability is selected, so as to predict the hotness of short video.

KEYWORDS

Short videos, video comment count prediction, machine learning algorithms

CITE THIS PAPER

Yimeng Ren, Bo Xiao, Machine Learning Based Short Video Comment Count Prediction. Automation and Machine Learning (2024) Vol. 5: 33-45. DOI: http://dx.doi.org/10.23977/autml.2024.050205.

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