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This study introduces a novel approach to generating temporal knowledge graphs annotated with sentiment analysis. Leveraging natural language processing and graph theory, we analyze how sentiment toward various topics evolves over time. Through a robust methodology, we identify key terms and their interrelationships. Our analysis unveils the progression of major themes, the terms with the highest sentiment growth, and the relationship between sentiment and academic impact. This work provides a comprehensive view of the emotional undertones in academic literature and offers insights into emerging trends and their potential implications.

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Patterns Beyond Words: Understanding Emotional Undertone on Knowledge Graphs

  • 会议论文

  • 发表日期：

    2026年

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  • Joao Tiago Aparicio,
  • Elisabete Arsenio,
  • Rui Henriques

摘要

This study introduces a novel approach to generating temporal knowledge graphs annotated with sentiment analysis. Leveraging natural language processing and graph theory, we analyze how sentiment toward various topics evolves over time. Through a robust methodology, we identify key terms and their interrelationships. Our analysis unveils the progression of major themes, the terms with the highest sentiment growth, and the relationship between sentiment and academic impact. This work provides a comprehensive view of the emotional undertones in academic literature and offers insights into emerging trends and their potential implications.