User-generated multimedia content—photos, text, videos, and audio—is becoming more and more common on social networking sites to allow individuals to express their thoughts. One of the largest and most advanced social media platform discussing ChatGPT is Twitter. This is because Twitter updates are constantly being produced and have a limited duration. The deep learning method for sentiment analysis of Twitter data about ChatGPT evaluation is presented in this research. This study used 4-class labels (sadness, joy, fear, and anger) from public Twitter data stored in the Kaggle database. The proposed deep learning strategy significantly improves the efficiency metrics determined by the use of the attention layer in current LSTM-RNN approaches, increasing accuracy by 20% and precision by 10-12%, but recall only 12-13%. Out of 18000 ChatGPT-related tweets, positive, neutral, and negative sentiments accounted for a respective 45%, 30%, and 35%. It is determined that the suggested deep learning technique for ChatGPT review sentiment categorization is effective, realistic, and fast to deploy
