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Computer Science > Computation and Language

arXiv:2308.11001 (cs)
[Submitted on 16 Aug 2023]

Title:Leveraging Explainable AI to Analyze Researchers' Aspect-Based Sentiment about ChatGPT

Authors:Shilpa Lakhanpal, Ajay Gupta, Rajeev Agrawal
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Abstract: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.
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2308.11001 [cs.CL]
  (or arXiv:2308.11001v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2308.11001

Submission history

From: Shilpa Lakhanpal [view email]
[v1] Wed, 16 Aug 2023 07:44:06 UTC (885 KB)
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