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Sentiment analysis (also known as opinion mining) is a natural language processing (NLP) technique for determining data’s positive, negative, or neutral nature. The rise of social media platforms such as X (formally Twitter) and Facebook have become great arenas for discourse on racism and mediums of racism ideologies. This study utilized a hybrid sentiment analysis to detect racist tweets using lexicon-based sentiment analysis and a Support Vector Machine. The models’ success in accurately classifying sentiments related to racism highlights its potential for broader applications in the analysis of other social issues. Furthermore, this study contributes to the ongoing discourse on combating racism in the digital age. By shedding light on the sentiments expressed online, it provides valuable insights that can inform policy decisions, advocacy efforts, and public awareness campaigns. The findings underscore the importance of addressing racism not just in the physical world but also in the digital sphere, where harmful ideologies can spread rapidly and widely.

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A Hybrid Sentiment Analysis Model to Detect Racist Tweets Using Lexicon-Based Sentiment Analysis and a Support Vector Machine Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Emmanuel Akwah Kyei,
  • Justice Williams Asare,
  • Prince Modey,
  • Martin Mabeifam Ujakpa,
  • Laizah Sashah Mutasa,
  • Emmanuel Freeman,
  • William Leslie Brown-Acquaye,
  • Lempogo Forgor,
  • Godfred Yaw Koi-Akrofi

摘要

Sentiment analysis (also known as opinion mining) is a natural language processing (NLP) technique for determining data’s positive, negative, or neutral nature. The rise of social media platforms such as X (formally Twitter) and Facebook have become great arenas for discourse on racism and mediums of racism ideologies. This study utilized a hybrid sentiment analysis to detect racist tweets using lexicon-based sentiment analysis and a Support Vector Machine. The models’ success in accurately classifying sentiments related to racism highlights its potential for broader applications in the analysis of other social issues. Furthermore, this study contributes to the ongoing discourse on combating racism in the digital age. By shedding light on the sentiments expressed online, it provides valuable insights that can inform policy decisions, advocacy efforts, and public awareness campaigns. The findings underscore the importance of addressing racism not just in the physical world but also in the digital sphere, where harmful ideologies can spread rapidly and widely.