Analysis of amazon product reviews using big data- apache pig tool
Автор: Amrit Pal Singh, Gurvinder Singh
Журнал: International Journal of Information Engineering and Electronic Business @ijieeb
Статья в выпуске: 1 vol.11, 2019 года.
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We live in the era of digital technologies where data is increasing day by day at a very high rate. The data is further popularly classified as ‘Big Data’ because of its velocity, veracity, variety and its huge volume. This data could be unstructured, semi-structured or structured as it is divergent in nature. In this work, we would assess various categories of Amazon Product Reviews, the large datasets that contain around 144 million reviews in total. The datasets consists of Product reviews collected from Amazon, each having various numbers of attributes of 11 different categories. The motive of this work is to find and compare the ratings of the products during the lifespan of the product reviews. Another goal of this work is to help Amazon regarding the listing of the products in their database. This work aims to relate user’s ratings and reviews to discover how beneficial and good a product is [6]. User ratings are collected and are analyzed based on different categories (datasets) which gives an insight as to which product performs good and what are the problems associated to a certain non-performing product.
Section I focusses on the framework of Hadoop, its nodes and its functioning structural architecture.. Further Section II explains the work carried out by McAuley and how Big data is used to solve the industry’s problems. A step by step working model of the work is explained in section III, briefly describing the datasets. Section IV depicts the results and outcomes of the work. The conclusion of the work is can be examined in Section V
Короткий адрес: https://sciup.org/15016159
IDR: 15016159 | DOI: 10.5815/ijieeb.2019.01.02
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