Technological progress has significantly enhanced convenience in daily life. Among these advancements, Natural Language Processing (NLP) has emerged as a groundbreaking innovation, particularly in the realm of large language models like Chat Generative Pre-trained Transformer (ChatGPT), leading to transformative improvements across diverse fields. Social networking platforms generate vast amounts of user-generated content containing rich sentiments, such as tweets, status updates, and blog posts. Analyzing the sentiments embedded in this data is highly valuable for understanding collective opinions on emerging technologies, brands, businesses, and more. Sentiment analysis provides organizations and stakeholders with a fast and efficient tool to monitor public sentiments, enabling them to identify areas of concern and address issues effectively. The proposed study examines the linguistic feature effectiveness to determine the tweet sentiment related to ChatGPT. The utility of lexical resources as well as features designed to capture the creative and informal language usually found in microblogging is then evaluated The study utilized the Valence Aware Dictionary for Sentiment Reasoning (VADER) for sentiment analysis that automates the process of labeling and polarity of tweets is classified (negative, positive or neutral). The word cloud was generated as well as topic modeling was performed using Latent Dirichlet Allocation (LDA). Analyzing user comments for ChatGPT, policymakers and related stakeholders can obtain important insights ultimately enabling them to optimize and improve the systems.