Sentiment Analysis on Existing Citations, Dissenting vs Confirming

Problem Statement:

Citations in academic manuscripts are not merely references; they often convey a specific relationship between the citing and cited works. Determining whether a citation indicates agreement, disagreement, or a neutral stance is crucial for understanding the evolution of scientific ideas and identifying potential controversies.

Challenge:

Develop a system that can analyze the context of citations within a scientific manuscript and classify the relationship between the citing and cited works as either agreement, disagreement, or neutral.

Detailed Description:

• Input:

  • A scientific manuscript (PDF or text).

  • A list of citations within the manuscript.

• System Functionality:

  • Context Extraction: Extract the sentences or paragraphs surrounding each citation.

  • Sentiment Analysis: Analyze the extracted context to determine the relationship between the citing and cited works.

  • Classification: Classify each citation as:

    • Agreement: The citing work supports or builds upon the cited work.

    • Disagreement: The citing work contradicts or challenges the cited work.

    • Neutral: The citing work simply refers to the cited work without expressing a clear opinion.

  • Output:

    • A list of citations with their corresponding sentiment classifications.

    • A structured report (e.g., JSON, CSV) summarizing the citation sentiment analysis.

• Example Scenarios:

  • "Smith et al. (2020) demonstrated a significant increase in... This finding is consistent with our results." (Agreement)

  • "Contrary to the findings of Jones (2018), we observed..." (Disagreement)

  • "This method was based on the approach described by Brown (2015)." (Neutral)

• Potential Technologies:

  • Natural Language Processing (NLP) for context extraction and sentiment analysis.

  • Sentence embedding models for semantic similarity analysis.

  • Machine learning classifiers for citation sentiment classification.

  • Large language models.

• Evaluation Metrics:

  • Precision, recall, and F1-score for each sentiment class.

  • Accuracy of classification.

  • Human evaluation of the system outputs.

Desired Outcomes:

• A functional system that can accurately classify citation sentiment.

• A dataset of citations with labeled sentiment classifications.

• Insights into the linguistic cues that indicate agreement, disagreement, or neutrality.