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Understanding Sentiment Across Genders: Challenges and Solutions

Author

Listed:
  • Hossein Hassani

    (International Institute for Applied Systems Analysis (IIASA), Austria)

  • Pouria Parvizi

    (Department of Computer Engineering, University of Kurdistan, Iran)

  • Mohammad Reza Yeganegi

    (International Institute for Applied Systems Analysis (IIASA), Austria)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

Abstract

The emergence of natural language processing models has made them a crucial part of financial and economic analysis, especially when it comes to understanding human behavior. Sentiment analysis has been used to understand people's opinions about policies, products, and services, as well as to gauge market sentiment. These insights can be used as input to other economic and financial models to enrich their performance. However, intertwining sentiment analysis with other analyses means that any inaccuracies or biases can negatively impact the final results, resulting in misleading conclusions. Specifically, any gender bias in sentiment analysis can lead to gender bias in subsequent analyses and final results. In this regard it is important to understand the potential sources of gender bias in sentiment analysis and address those biases. This study aims to provide a comprehensive understanding of gender bias in sentiment analysis and explore strategies to mitigate it, ensuring unbiased results.

Suggested Citation

  • Hossein Hassani & Pouria Parvizi & Mohammad Reza Yeganegi & Rangan Gupta, 2025. "Understanding Sentiment Across Genders: Challenges and Solutions," Working Papers 202515, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202515
    as

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