Prediction of Twitter Sentiment Analysis by Using Naive Bayes Algorithm

Abstract

The purpose of our study is to do a sentiment analysis of tweets to determine whether they are positive, negative, or neutral. Many businesses use sentiment analysis to determine how well their products and companies are performing in the marketplace. In a burgeoning discipline, sentiment analysis is now being used to predict the outcome of political elections and polls. Twitter is an internet platform that allows users to send tweets with a maximum of 140 characters. We intend to get a representation of public opinion by analyzing the sentiments conveyed in the tweets due to the enormous amount of data we are using. This research investigates sentiment analysis, which involves extracting many tweets and analyzing them using a machine-learning technique like the naive Bayes classifier. Results are classified as several positive, negative or neutral and it is also expressed in the pie chart and bar chart. With the speedup development of websites, informal organizations, websites, online entries, surveys, feelings, proposals, evaluations, and criticism are created by scholars. This author created opinion content that can be about books, individuals, lodgings, items, research, occasions, and so on. These sentiments become extremely useful for organizations, state-run administrations, and people. While this content is intended to be helpful, a greater part of this essayist's produced content requires utilizing the message mining procedures and feel analysis. In any case, there are a few difficulties confronting the opinion examination and assessment process. These difficulties become snags in dissecting the exact significance of feelings and distinguishing the appropriate opinion extremity. Opinion analysis is the act of applying regular language handling and text examination strategies to recognize and remove abstract data from text. This paper presents a review of the feeling examination challenges pertinent to their methodologies and strategies