Viewing Prediction Based on Hybrid Kernel Model with User Behaviors and Sentiment Analysis

Meiqi Ji, Xiaoli Feng, Ruiling Fu, Fulian Yin

2022

Abstract

The viewing prediction for TV programs has an important impact on users and program producers and broadcasters, but existing prediction methods do not take into account the emotional factors of users’ viewing and have problems of over-fitting. In this paper, we propose a hybrid kernel least squares support vector machine model based on a particle swarm optimization algorithm to research viewing prediction, based on the advantages of time series and least squares support vector machine models in prediction, taking into account two types of factors, namely user viewing behavior and comment sentiment, and setting up a comparison experiment. The results show the effectiveness and applicability of the model in fitting and predicting audience ratings.

Paper Citation

in Harvard Style

Ji M., Feng X., Fu R. and Yin F. (2022). Viewing Prediction Based on Hybrid Kernel Model with User Behaviors and Sentiment Analysis. In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI; ISBN 978-989-758-620-0, SciTePress, pages 197-202. DOI: 10.5220/0011733000003607

in Bibtex Style

@conference{icpdi22,
author={Meiqi Ji and Xiaoli Feng and Ruiling Fu and Fulian Yin},
title={Viewing Prediction Based on Hybrid Kernel Model with User Behaviors and Sentiment Analysis},
booktitle={Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI},
year={2022},
pages={197-202},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011733000003607},
isbn={978-989-758-620-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI
TI - Viewing Prediction Based on Hybrid Kernel Model with User Behaviors and Sentiment Analysis
SN - 978-989-758-620-0
AU - Ji M.
AU - Feng X.
AU - Fu R.
AU - Yin F.
PY - 2022
SP - 197
EP - 202
DO - 10.5220/0011733000003607
PB - SciTePress