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Area of Science:

  • Natural Language Processing (NLP)
  • Artificial Intelligence (AI)
  • Sociolinguistics

Background:

  • Automated sentiment analysis is widely used for trend detection across various domains.
  • Natural Language Processing (NLP) techniques, including sentiment analysis, can inadvertently perpetuate societal biases.
  • Existing research has identified bias in sentiment analysis concerning gender, ethnicity, and disability.

Purpose of the Study:

  • To investigate bias in popular sentiment analysis tools specifically concerning queer identities.
  • To expand upon existing research by examining a broader range of marginalized groups within sentiment analysis.
  • To provide guidance on selecting sentiment analysis tools to mitigate bias.

Main Methods:

  • Evaluation of six popular sentiment analysis tools.
  • Testing tools with sentences related to various queer identities.
  • Comparative analysis of tool responses to identify biased outputs.

Main Results:

  • Evidence of bias against several marginalized queer identities was found across tested tools.
  • Two prominent models (Google, Amazon) showed bias despite apparent superficial debiasing efforts.
  • The extent and nature of bias varied among the evaluated sentiment analysis tools.

Conclusions:

  • Sentiment analysis tools are not universally unbiased and can disadvantage marginalized communities.
  • Superficial debiasing methods may be insufficient to eliminate bias in AI models.
  • Selecting sentiment analysis tools requires careful consideration of potential biases to ensure reliable and equitable results.