Sentiment Analysis on ChatGPT

Abstract—This research paper explores social media analytics to analyze human behavior and public sentiment on microblogging platforms regarding major Artificial Intelligence advancements.   Focusing on the launch of OpenAI's ChatGPT, the study extracts a text dataset in Python (via Visual Studio) using libraries such as snscrape and langdetect to isolate English-language content. The textual data is then processed and analyzed in RStudio. A Word Cloud analysis is performed using tools such as wordcloud2 to map and discuss the most frequently occurring terms and concepts associated with the AI system. Furthermore, a granular sentiment analysis is executed, categorizing user emotions into ten distinct dimensions: anger, anticipation, disgust, fear, joy, negative, positive, sadness, surprise, and trust.   The paper concludes with a comprehensive discussion of audience reception trends and offers key strategic recommendations on the societal impact, job-market implications, and future governance of rapidly evolving AI technologies.  

Index Terms—Artificial Intelligence, ChatGPT, Twitter, Sentiment Analysis, Natural Language Processing