Study of Different Algorithms on Opinion Mining

Abstract

Abstract: Social media is critical in today's world for exchanging information and disseminating ideas. A person's emotional impact has a significant impact on their day-to-day life. Sentiment analysis is a form of text mining that locates and pulls out subjective information from sources, allowing a company to track discussions online and monitor social sentiment about their brand, product, or service. Simply put, sentiment analysis helps determine the author's attitude towards a topic. Positive, neutral, or negative pieces of writing are classified by sentiment analysis software. Deep learning algorithms and various functions of natural language processing helps to interpret the written or spoken sentiments regarding a topic. An ecosystem where millions of bytes of data are produced daily has enabled sentiment analysis to be a key tool for interpreting these huge chunks of data. The purpose of this work is to conduct a sentiment analysis on "tweets" by making use of a variety of machine learning algorithms. The study will make an attempt to categorise the polarity of the tweet as either positive, negative, or neutral. In the event that a tweet has only positive, negative, or neutral components, the label assigned to the tweet will be determined by the sentiment that predominates.