Deciphering User Perspectives and Prejudices: A Comparison of Models to Investigate Sentiments on ChatGPT on Twitter

The capacity of ChatGPT (Chat Generative Pretrained Transformer) to produce responses that resemble those of a human has led to its rise in popularity. It is important to remember that using ChatGPT excessively or blindly can have negative effects, particularly when making important decisions. In a similar vein, a lack of confidence in the technology might result in underutilization and lost possibilities. This study explores a crucial component of this interaction: how user perception affects ChatGPT's efficacy. Although ChatGPT is clearly technologically advanced, its effectiveness depends on how users view its capabilities, limits, and replies. The abstract delves into the complex interplay between user perception and system performance, highlighting the critical roles played by ethical considerations, contextual knowledge, accurate responses, and transparency. The study recognizes the difficulties caused by system constraints and possible misconceptions, but it also highlights the importance of user happiness, trust, and acceptance. Through an analysis of the relationship between user perception and ChatGPT's efficacy, this abstract offers important new perspectives on how to best optimize human-AI interactions for improved user experiences. ChatGPT Has been widely used by the users from its launch. Many users have trust and different types of interaction patterns as well as user experience for enhancing their content creation in different types of careers. This study uses the ai platform for understanding its user perception with the use of tweets for examining their perceptions about this chat GPT. The proposed hybrid model is compared with random forest and svm technique for high accuracy in understanding user perceptions based on text analysis from Twitter.