WITHDRAWN: ChatGptTweets Analyses Based On AI

Abstract Sentiment analysis plays a crucial role in understanding public opinions and attitudes. In this study, we address the sentiment analysis of ChatGPT tweets, leveraging a diverse set of machine learning models. The dataset comprises user-generated tweets directed at ChatGPT, reflecting a spectrum of sentiments ranging from positive endorsements to negative critiques. Sentiment analysis is the process of utilizing text analysis tools to extract and classify sentiments conveyed in text data. As demonstrated by past research, sentiment analysis of conversational agent interactions, such as those with ChatGPT, has a great deal of potential to offer insightful information to help developers and users understand the perception and effectiveness of these agents. This understanding contributes to improving user experience and refining the capabilities of conversational agents like ChatGPT. For sentiment analysis in this context, the current systems either use a learning-based method or a lexicon-based approach. While lexicon-based techniques tend to be domain-specific, limiting their wide application, learning-based techniques require annotated data. In order to get better results, this research adopts a hybrid method that combines lexicon-based and learning-based strategies. General-purpose lexicons of sentiment are used, and the tweets are annotated using tools such as Text Blob. Moreover, term frequency-inverse document frequency (TF-IDF), a feature engineering technique, has been included to extract important features. Ultimately, the sentiments of the tweets are classified using a variety of learning models, such as Machine Learning with logistic regression (LR), random forest (RF), decision tree (DT), gradient boosting (GB), and multilayer perceptron (MLP). The suggested hybrid approach's effectiveness is assessed using the F1-score, accuracy, precision, and recall metrics. According to experimental findings, combining lexicon-based and learning-based strategies yields better outcomes than each one used alone. Additionally, Text Blob has demonstrated encouraging results, with 99 percent accuracy achieved with MLP.