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Sentimental analysis of Amazon reviews using naive bayes on laptop products with MongoDB and R

机译:亚马逊评论的感伤分析,使用朴氏和r的笔记本电脑产品上使用天真贝

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Start In Today's era the e-commerce is developing rapidly these years, buying products on-line has become more and more fashionable owing to its variety of options, low cost value (high discounts) and quick supply systems, so abundant folks intend to do online shopping. In the meantime the standard and delivery of merchandise is uneven, fake branded products are delivered. We use product users review comments about product and review about retailers from Amazon as data set and classify review text by subjectivity/objectivity and negative/positive attitude of buyer. Such reviews are helpful to some extent, promising both the shoppers and products makers. This paper presents an empirical study of efficacy of classifying product review by tagging the keyword. In the present study, we tend to analyse the fundamentals of determining, positive and negative approach towards the product. Thus we hereby propose completely different approaches by removing the unstructured data and then classifying comments employing Naive Bayes algorithm.
机译:从今天的时代开始,电子商务随着这些年的发展迅速,由于各种选择,低成本(高折扣)和快速供应系统,购买产品在线迅速发展地变得越来越时尚。网上购物。与此同时,商品的标准和交付是不平衡的,假品牌产品被交付。我们使用产品用户审查关于产品的评论,并查看亚马逊的零售商作为数据集,并按主观/客观性和买家的负面/正态度分类评论文本。此类评论在某种程度上有助于,承诺购物者和产品制造商。本文介绍了通过标记关键字进行分类产品审查的疗效的实证研究。在本研究中,我们倾向于分析对产品的确定,积极和负面方法的基础。因此,我们通过删除非结构化数据然后进行分类评论来提出完全不同的方法,然后采用Naive Bayes算法。

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