Product Promotion Prediction Model Based on Evaluation Information

Lijie Hou, Qixiu Kang
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Abstract

This paper mainly studies the impact of evaluation information on e-commerce platform on the future of products. Through natural language processing and rating, an evaluation model based on user rating and evaluation is defined to measure product quality. Among them, evaluations are differentiated: review sentiment coefficient (R) and review length (L).The evaluation model is:D=0.3*S+0.7*( 0.3*L+0.7*R). In order to predict the future reputation of products, based on the above evaluation model, time series is used to rank the products studied. Each customer purchases the product through Markov chain model, so as to predict the probability of future word-of-mouth spread of the product. Use TOPSIS method to select monthly sales, stars and comment sentiment coefficient as indicators. The comprehensive measurement method based on text and score is determined to predict whether the product is successfully promoted.
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基于评价信息的产品推广预测模型
本文主要研究电子商务平台上的评价信息对产品未来的影响。通过自然语言处理和评分,定义了基于用户评分和评价的产品质量评价模型。其中,评价分为评论情感系数(R)和评论长度(L),评价模型为:D=0.3*S+0.7*(0.3*L+0.7*R)。为了预测产品未来的声誉,在上述评价模型的基础上,使用时间序列对所研究的产品进行排名。每个顾客通过马尔可夫链模型购买产品,从而预测产品未来口碑传播的概率。采用TOPSIS方法选取月销量、星级、评论情绪系数作为指标。确定基于文本和分数的综合测量方法来预测产品是否成功推广。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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