Sentiment Analysis on Amazon Product Review

B. Kamalesh, S. Vijayalakshmi
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引用次数: 3

Abstract

Users throughout the world may now access massive amounts of data thanks to the internet and social media platforms. [5] In every facet of human existence, electronic commerce (e-commerce) plays a crucial role. E-commerce is a marketing approach that enables businesses and consumers to buy and sell things via the internet. When buyers look for product information and compare alternatives online, they generally have access to dozens or hundreds of product reviews from alternative shoppers. Machine learning is the most appropriate approach to training a neural network in today’s age of practical artificial intelligence. So implementing a model to polarize those reviews and learn from them would make passing hundreds of comments a lot easier. [24] The interpretation will be a very basic product with positive, neutral, and negative polarization. The product is checked. This study suggests a sentiment evaluation model for shopper reviews based on the object and emotive word mining for emotional level analysis using machine learning approaches.
亚马逊产品评论的情感分析
由于互联网和社交媒体平台,世界各地的用户现在可以访问大量数据。[5]在人类生活的方方面面,电子商务都起着至关重要的作用。电子商务是一种营销手段,它使企业和消费者能够通过互联网买卖东西。当买家在网上寻找产品信息并比较替代品时,他们通常可以从其他购物者那里获得数十或数百条产品评论。在当今实用的人工智能时代,机器学习是训练神经网络最合适的方法。因此,实现一个模型来区分这些评论并从中学习,将使传递数百条评论变得容易得多。[24]口译将是一个非常基本的产品,具有正、中性和负极化。检查产品。本研究提出了一种基于对象的购物者评论情感评价模型和使用机器学习方法进行情感水平分析的情感词挖掘模型。
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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