Abstract
Sentiment analysis is a subfield of natural language processing (NLP) that aims to determine the emotional tone and sentiment expressed in a given piece of text. It plays a crucial role in understanding the opinions, attitudes, and emotions of individuals towards various subjects, products, or events. With the rapid growth of online communication and social media, sentiment analysis has become increasingly important for businesses, governments, and researchers to gain valuable insights into public sentiment and make data-driven decisions. Deep Learning, a branch of machine learning, has shown remarkable success in various NLP tasks, including sentiment analysis. This research explores role of sentiment analysis in twitter data, application of deep learning techniques in sentiment analysis, focusing on recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models. In this paper sentiment analysis perform on a dataset of tweets related to the Pfizer vaccine. Here we have data related to vaccination tweets of 11021 users and sentiments of users. It processes text input, classifies feelings, and visualizes the findings using multiple natural language processing techniques and machine learning algorithms.