基于用户收视行为与评论情感分析的收视预测研究  

Rating prediction based on user viewing behavior and comment emotion analysis

作　　者：冯小丽 吴肇良 殷复莲[1] FENG Xiaoli;WU Zhaoliang;YIN Fulian(State Key Laboratory of Media Convergence and Communication School of Information and Communication Engineering,Communication University of China,Beijing 100024,China)

机构地区：[1]媒体融合与传播国家重点实验室,中国传媒大学信息与通信工程学院,北京100024

出　　处：《中国传媒大学学报（自然科学版）》2022年第1期45-51,共7页Journal of Communication University of China：Science and Technology

基　　金：国家自然科学基金(61801440)。

摘　　要：节目收视预测对提高用户体验起到越来越重要的作用,而针对现有收视预测往往仅考虑用户收视行为忽略了用户评论情感因素,以及要求数据量丰富、易受“奇异点”影响、存在过拟合、欠拟合、参数设置困难等问题。本文提出基于粒子群优化算法的混合核最小二乘支持向量机模型,综合考虑了用户收视行为、评论情感两类因素,并结合时间序列及最小二乘支持向量机模型在预测上的优势对节目收视进行预测。本文采用自适应迭代预测方式,以20天为滑动窗口步长,对用户收视序列进行拟合训练,验证了该模型在收视预测上的有效性及适用性。Program rating prediction plays an increasingly important role in improving the user experience.However,existing rating prediction only considers user rating behavior and ignores the emotional factors from user comments.Moreover,it requires abundant data,is susceptible to the influence of“singularity”,and has problems such as over-fitting,under-fitting and difficult parameter setting.In this paper,a hybrid kernel least squares support vector machine model based on particle swarm optimization algorithm is proposed,which comprehensively considers the two factors of user viewing behavior and comment emotion,and combines the advantages of time series and least-squares support vector machine model in forecasting to predict program viewing.In this paper,the adaptive iterative forecasting method is adopted,and the 20-day sliding window step is used to fit the user viewing sequence,which verifies the effectiveness and applicability of the model in viewing forecast.

关 键 词：用户行为 评论情感 收视预测 混合核最小二乘支持向量机 粒子群优化算法 

分 类 号：O422[理学—声学]