Sentiment Analysis to Identify Consumer Criticism of Artificial Intelligence: A ChatGPT Case Study

Abstract

As artificial intelligence becomes increasingly integrated into various aspects of our lives, understanding consumer sentiment and criticism towards artificial intelligence technologies becomes pivotal for effective utilization. This study presents a case study focusing on ChatGPT, a popular AI application, as a means to identify consumer criticism of AI use in businesses. By harnessing sentiment analysis and clear analytics, administrators can enhance their understanding of consumer feedback and thereby improve AI integration for better user experiences. Increased consumer satisfaction is important to overall business to consumer relationships and streamlined AI use will facilitate company procedures. Our methodology revolves around machine learning techniques, specifically utilizing four classifiers, Keras logistic regression, Naive Bayes, Support Vector Machine, and random forest regressor, alongside two numerical feature representations, Bag-of-Words and Term Frequency-Inverse Document Frequency. The results show that the Term Frequency-Inverse Document Frequency features combined with the random forest regressor yielded the strongest performance in identifying criticism of ChatGPT-related media, with F1 scores of 100 and 99 percent for no criticism and criticism, respectively.