  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.
