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

TL;DR

This paper introduces an Explainable AI-based methodology to analyze researchers' aspect-based sentiment towards ChatGPT, addressing limitations of traditional sentiment analysis on short texts and providing deeper insights into research community opinions.

Contribution

It proposes a novel Explainable AI approach for aspect-based sentiment analysis on research data, extending analysis capabilities to longer and more complex texts.

Findings

Enhanced sentiment analysis on research datasets

Insights into researchers' perceptions of ChatGPT

Improved analysis of longer textual data

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.