Social Media Sentiment Analysis Using Twitter Dataset

Social media platforms are essential in today’s digital age for exchanging ideas on a variety of subjects. Twitter’s quick 280-character message exchanges have made it more and more popular, and this makes it a great way to gauge public opinion. Companies use Twitter to gain insight into customer viewpoints, which are critical for success in the marketplace. This study focuses on sentiment analysis, identifying the emotions conveyed in tweets by utilizing a Twitter dataset. The creation of software for sentiment analysis is a computer method for comprehending customer views. The architecture of the sentiment analysis system, which is intended to extract a substantial number of tweets, is thoroughly explained by this paper. Prototyping techniques are used in software development. The study uses a pie chart on an interactive website to present its findings. It divides tweets containing consumer feedback into “positive” and “negative” categories. Businesses can use this graphic to gain insights into customer sentiment and improve their marketing and product decisions. The sentiment analysis tool has potential, however there are problems with its integration. For instance, Django compatibility problems prevented it from serving as planned as a web application, necessitating additional work. This study emphasizes the importance of examining Twitter attitudes for business decision-making, albeit its limitations. It also highlights how important it is to keep developing and refining sentiment analysis methods.