Sentiment Analysis of Citations in Scientific Articles Using ChatGPT: Identifying Potential Biases and Conflicts of Interest

TL;DR

This paper explores using ChatGPT for sentiment analysis of citations in scientific articles to identify biases and conflicts of interest, aiming to improve objectivity in scholarly evaluations.

Contribution

It introduces a novel application of ChatGPT for nuanced citation sentiment analysis and bias detection in scientific literature.

Findings

ChatGPT effectively discerns positive and negative citation sentiments.

The approach identifies potential biases and conflicts of interest.

AI-powered citation analysis enhances research integrity.

Abstract

Scientific articles play a crucial role in advancing knowledge and informing research directions. One key aspect of evaluating scientific articles is the analysis of citations, which provides insights into the impact and reception of the cited works. This article introduces the innovative use of large language models, particularly ChatGPT, for comprehensive sentiment analysis of citations within scientific articles. By leveraging advanced natural language processing (NLP) techniques, ChatGPT can discern the nuanced positivity or negativity of citations, offering insights into the reception and impact of cited works. Furthermore, ChatGPT's capabilities extend to detecting potential biases and conflicts of interest in citations, enhancing the objectivity and reliability of scientific literature evaluation. This study showcases the transformative potential of artificial intelligence (AI)-powered tools in enhancing citation analysis and promoting integrity in scholarly research.