Leveraging Explainable AI to Analyze Researchers' Aspect-Based Sentiment about ChatGPT
This work addresses the need for better sentiment analysis in the research community regarding ChatGPT's usage aspects, though it appears incremental in applying existing techniques to a new domain.
The paper tackles the problem of analyzing researchers' aspect-based sentiment about ChatGPT by proposing a methodology using Explainable AI, which extends Aspect-Based Sentiment Analysis to handle longer text data on new datasets.
The groundbreaking invention of ChatGPT has triggered enormous discussion among users across all fields and domains. Among celebration around its various advantages, questions have been raised with regards to its correctness and ethics of its use. Efforts are already underway towards capturing user sentiments around it. But it begs the question as to how the research community is analyzing ChatGPT with regards to various aspects of its usage. It is this sentiment of the researchers that we analyze in our work. Since Aspect-Based Sentiment Analysis has usually only been applied on a few datasets, it gives limited success and that too only on short text data. We propose a methodology that uses Explainable AI to facilitate such analysis on research data. Our technique presents valuable insights into extending the state of the art of Aspect-Based Sentiment Analysis on newer datasets, where such analysis is not hampered by the length of the text data.