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Abstract

The internet is a very effective resource for solving all problems in the present era. The world's population as a whole spends one-third of their time and money using the internet. People learn things from it in every aspect of life, including education, entertainment, communication, shopping, etc. In order to achieve this, consumers take use of websites and share comments or opinions about various goods, services, events, etc. based on their personal experiences. In this way, the input from those webs is composed into a sizable amount of textual data that can be investigated, assessed, and controlled for the decision-making process. Natural Language Processing (NLP) and the extraction of the key theme are both aspects of opinion mining (OM). It's crucial to get feedback on our ideas from people in the form of good, negative, and neutral remarks. Researchers are now presenting information in the form of summaries that will benefit a variety of people. Since the 1950s, the research community has generated automatic summaries, but there are two distinct types of automation: abstractive and extractive approaches. A brief summary of opinion mining's automation process approaches and summarization techniques is provided in this paper.