Abstract
Opinion mining, also known as sentiment analysis, aims to extract and summarize opinions. It is one of the extremely challenging topics in modern information analysis, from both an empirical and a theoretical perspective. For a web-based system or any organization, it is necessary to conduct opinion polls, surveys, and focus groups in order to gather public opinions about its products and services to aid their business in various ways. However, finding and monitoring opinion on the web and distilling the information contained in them remains a challenging task due to the unstructured nature of the web and heterogeneous content stored in it. Each site may contain a large volume text with opinion embedded within them. It is difficult to analyze each and every location/page of the web manually and hence there is a need of an automated mining technique that focuses on extraction and evaluation of opinions from the web content retrieval. This work presents an automated technique for feature-based opinion mining with system architecture, detail methodology, and results.
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