A Zero-Shot Aspect-Based Sentiment Analysis of Public Perception Toward AI Chatbots - Fauzan, Naufal Andila (Universitas Pendidikan Nasional, 2025)
The rapid development of AI chatbots has sparked public discussion on social media regarding their performance, ethical implications, and related concerns. While past studies primarily focused on individual chatbot model using traditional sentiment analysis, this study implements a novel application of Zero-Shot Aspect-Based Sentiment Analysis (ABSA) on 17,562 tweets mentioning AI chatbots such as ChatGPT, Bard (now Gemini), and DeepSeek, utilizing an efficient sentiment extraction method without supervised training. Six aspects were analyzed to understand the sentiment pattern and the results show the discussion was dominated by negative sentiment, with Bard receiving the most positive sentiment, potentially shaped by brand trust and user familiarity. On the other hand, DeepSeek and ChatGPT attracted more criticism, especially related to performance and bias aspects. This study offers data-driven suggestions for developers, including improving response accuracy to shape user trust, reducing biased output, and developing real-time discourse analysis. Future work should incorporate multiple platforms to avoid bias, analyze more AI chatbot models, and include temporal sentiment for broader insights.