基于在线评论的预测模型构建研究

Research on Predictive Model Construction Based on Online Reviews

摘要: 本研究的目的在于将传统的年限数据以及在线评论等非传统数据一起考量，建立会员销量预测模型，提高销售预测的准确性和可靠性。我们收集了哔哩哔哩大会员的历史销售数据和相关的在线评论数据，采用机器学习方法对数据进行分析和建模。实验结果表明，基于在线评论的销量预测模型相比传统的历史销售数据预测模型，在预测准确性和可靠性方面都有了显著的提升。这表明在线评论数据可以为销售预测提供有价值的信息，对企业制定销售策略具有重要意义。本研究的研究成果可为电商平台和其他企业提供指导，也可为相关学科领域的研究提供有益的借鉴。

Abstract: The purpose of this study is to establish a member sales forecast model by considering traditional age data and non-traditional data such as online reviews, and to improve the accuracy and reliability of sales forecasting. We collected historical sales data and related online review data from Bilibili members, and used machine learning methods to analyze and model the data. Experimental results show that the sales forecasting model based on online reviews has significantly improved the prediction accuracy and reliability compared with the traditional historical sales data forecasting model. This shows that online review data can provide valuable information for sales forecasting and is important for businesses to develop sales strategies. The research results of this study can provide guidance for e-commerce platforms and other enterprises, and can also provide useful references for research in related disciplines.