Sentiment Analysis using Different Machine Learning Techniques for Product Review

Ruqaiya Khanam, Abhishek Sharma
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Abstract

The World Wide Web (WWW) has turned into an immense wellspring of crude information produced by both the customers and the clients. Utilizing online media, web - based business sites, and film surveys, for example, Facebook, Twitter, Amazon, Flip kart and so forth, clients share their perspectives and emotions in an advantageous way. In WWW, where many individuals express their perspectives in their day by day connection, by the same token in the web-based media or in e-commerce which can be their opinions , what’s more, sentiments about a specific thing. Sentiment analysis is characterized as the way toward mining information, reviewing or anticipating the feelings or emotions behind the sentence via natural language processing. This paper includes two approaches, the first one is a lexicon-based approach, and the second one is machine learning technique logistic regression to extract the sentiments from text.
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使用不同机器学习技术进行产品评论的情感分析
万维网(WWW)已经成为顾客和客户共同产生的原始信息的巨大源泉。利用在线媒体、基于网络的商业网站和电影调查,例如Facebook、Twitter、亚马逊、flipkart等,客户以一种有利的方式分享他们的观点和情感。在WWW中,许多人在日常联系中表达自己的观点,同样的,在网络媒体或电子商务中,可以是他们的观点,更重要的是,对特定事物的看法。情感分析的特点是通过自然语言处理挖掘信息,回顾或预测句子背后的感受或情绪。本文包括两种方法,一种是基于词典的方法,另一种是基于机器学习技术的逻辑回归从文本中提取情感。
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