Sentiment Analysis of Citations in Scientific Articles Using ChatGPT: Identifying Potential Biases and Conflicts of Interest
- URL: http://arxiv.org/abs/2404.01800v2
- Date: Sat, 6 Apr 2024 19:08:08 GMT
- Title: Sentiment Analysis of Citations in Scientific Articles Using ChatGPT: Identifying Potential Biases and Conflicts of Interest
- Authors: Walid Hariri,
- Abstract summary: This article introduces the innovative use of large language models, particularly ChatGPT, for comprehensive sentiment analysis of citations within scientific articles.
ChatGPT can discern the nuanced positivity or negativity of citations, offering insights into the reception and impact of cited works.
- Score: 4.13365552362244
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts [49.97673761305336]
We evaluate three large language models (LLMs) for their alignment with human narrative styles and potential gender biases.
Our findings indicate that, while these models generally produce text closely resembling human authored content, variations in stylistic features suggest significant gender biases.
arXiv Detail & Related papers (2024-06-27T19:26:11Z) - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
ResearchAgent is a large language model-powered research idea writing agent.
It generates problems, methods, and experiment designs while iteratively refining them based on scientific literature.
We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
arXiv Detail & Related papers (2024-04-11T13:36:29Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews.
The newly emerging AI-generated literature reviews are also appraised.
This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - Artificial intelligence technologies to support research assessment: A
review [10.203602318836444]
This literature review identifies indicators that associate with higher impact or higher quality research from article text.
It includes studies that used machine learning techniques to predict citation counts or quality scores for journal articles or conference papers.
arXiv Detail & Related papers (2022-12-11T06:58:39Z) - Automatic Analysis of Linguistic Features in Journal Articles of
Different Academic Impacts with Feature Engineering Techniques [0.975434908987426]
This study attempts to extract micro-level linguistic features in high- and moderate-impact journal RAs, using feature engineering methods.
We extracted 25 highly relevant features from the Corpus of English Journal Articles through feature selection methods.
Results showed that 24 linguistic features such as the overlapping of content words between adjacent sentences, the use of third-person pronouns, auxiliary verbs, tense, emotional words provide consistent and accurate predictions for journal articles with different academic impacts.
arXiv Detail & Related papers (2021-11-15T03:56:50Z) - CitationIE: Leveraging the Citation Graph for Scientific Information
Extraction [89.33938657493765]
We use the citation graph of referential links between citing and cited papers.
We observe a sizable improvement in end-to-end information extraction over the state-of-the-art.
arXiv Detail & Related papers (2021-06-03T03:00:12Z) - Semantic Analysis for Automated Evaluation of the Potential Impact of
Research Articles [62.997667081978825]
This paper presents a novel method for vector representation of text meaning based on information theory.
We show how this informational semantics is used for text classification on the basis of the Leicester Scientific Corpus.
We show that an informational approach to representing the meaning of a text has offered a way to effectively predict the scientific impact of research papers.
arXiv Detail & Related papers (2021-04-26T20:37:13Z) - Enhancing Scientific Papers Summarization with Citation Graph [78.65955304229863]
We redefine the task of scientific papers summarization by utilizing their citation graph.
We construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains.
Our model can achieve competitive performance when compared with the pretrained models.
arXiv Detail & Related papers (2021-04-07T11:13:35Z) - A Decade of In-text Citation Analysis based on Natural Language
Processing and Machine Learning Techniques: An overview of empirical studies [3.474275085556876]
Information scientists have gone far beyond traditional bibliometrics by tapping into advancements in full-text data processing techniques.
This article aims to narratively review the studies on these developments.
Its primary focus is on publications that have used natural language processing and machine learning techniques to analyse citations.
arXiv Detail & Related papers (2020-08-29T17:27:08Z) - ImpactCite: An XLNet-based method for Citation Impact Analysis [4.526582372434088]
Impact analysis enables us to quantify the quality of the citations.
XLNet-based solution ImpactCite achieves a new state-of-the-art performance for both citation intent and sentiment classification.
Additional efforts have been performed to come up with CSC-Clean corpus, which is a clean and reliable dataset for citation sentiment classification.
arXiv Detail & Related papers (2020-05-05T08:31:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.