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1、分析所提供的三个产品数据集,以确定、描述和支持数学证据、有意义的定量和/或定性模式、关系、衡量标准以及星级、评论和有益性评级之间的参数,这将有助于阳光公司在其三个新的在线市场产品中取得成功。
对于问题1,确定星级、评论和有益性评级三者之间的关系和参数,可以用神经网络的知识,将星级、评论和有益性评级作为输入神经元,最终的阳光公司产品线上销售是否成功作为输出神经元。
2.使用您的分析来回答阳光公司营销总监的以下具体问题和要求:
a.根据最能提供以下信息的评级和评论,确定数据衡量标准。阳光公司跟踪,一旦他们的三个产品在网上市场上销售。
问题a首先要将评论进行数据化,利用Python的NLP自言语言处理,对评论0表示消极的评论,1表示积极的评论,然后将评论和星级两列数据归一化,去掉一列可以,简化数据。也可以通过权重比,比如评论占30%,星级占据70%。
b.确定并讨论每个数据集内基于时间的度量和模式,这些度量和模式可能表明一个产品在网上市场的声誉在增加或减少。
采用时间序列模型,看看评级和评论随时间的变化。
c确定基于文本的衡量标准和基于评级的衡量标准的组合,这两种衡量标准最能表明产品可能成功或失败。
用模糊评价判断方法来衡量产品的成功与否。
d.更高的星级会引发更多的评论吗?例如,在看到一系列低星级后,客户更有可能写一些类型的评论吗?
还是星级与评论之间的关联关系。根据星级来看评论怎么样
e基于文本的评论的具体质量描述符,如“热情”、“失望”和其他,是否与评级水平密切相关?
根据评论,来看星级怎么样。