Tweet Analysis: Visualisation using Python

Abstract Most of the information is open to the public and can be mined. Huge amounts of data are available on the social networking site like Twitter. This data may be unstructured, semi-structured, or structured in any combination. This enables users to discuss concerns with various communities, as well as to share and express their opinions about various subjects. Sentiment analysis of Twitter data has been the subject of extensive research. Sentiment: that describes how people feel or think about a specific subject. Not facts, but subjective perceptions of the domain are the result. Twitter is one of the most widely used platforms. Many scholars who conduct research in critical domains such as consumer brands, market performance, and democratic election prediction have found inspiration from Twitter. The initiative focuses on Twitter data analysis. This paper focuses on a tool to assess public sentiment, monitor crises, conduct market research, and the influence and reach of a Twitter trend, it lets users view a range of metrics related to any hashtag, such as the most popular users utilizing it, users with the greatest reach, an estimate of the hashtag's actual and maximum reach, the timely frequency of tweets and retweets, and the estimated market value of the hashtag with the use of python and Twitter API. It will help combat misinformation and online negativity and promote accountability with a focus on visual content analysis.