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This study compares various methods of verifying adherence of Wikipedia articles to the Neutral Point of View (NPOV) standard. To gauge potential bias, we apply four sentiment analysis models - two lexicon-based (TextBlob, VADER) and two transformer-based (RoBERTa, DistilBERT) - to nearly 7 million articles selected from the English Wikipedia. The articles are pre-processed, categorized by topic and quality, according to proposed methodology. The results showed that sentiment of Wikipedia articles varies by topic and that proper model selection is crucial for accurate NPOV assessment. The paper contributes a practical framework for implementing sentiment analysis on longer texts. We also offer insights into how article quality correlates with sentiment outcomes.

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Cross-Topic Sentiment Analysis of Wikipedia Articles: A Comparative Study of AI Models

  • 会议论文

  • 发表日期：

    2026年

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  • Włodzimierz Lewoniewski,
  • Milena Stróżyna,
  • Izabela Czumałowska,
  • Aleksandra Wojewoda,
  • Krzysztof Węcel

摘要

This study compares various methods of verifying adherence of Wikipedia articles to the Neutral Point of View (NPOV) standard. To gauge potential bias, we apply four sentiment analysis models - two lexicon-based (TextBlob, VADER) and two transformer-based (RoBERTa, DistilBERT) - to nearly 7 million articles selected from the English Wikipedia. The articles are pre-processed, categorized by topic and quality, according to proposed methodology. The results showed that sentiment of Wikipedia articles varies by topic and that proper model selection is crucial for accurate NPOV assessment. The paper contributes a practical framework for implementing sentiment analysis on longer texts. We also offer insights into how article quality correlates with sentiment outcomes.