Potential Pitfalls With Automatic Sentiment Analysis: The Example of Queerphobic Bias

Abstract

Automated sentiment analysis can help efficiently detect trends in patients’ moods, consumer preferences, political attitudes and more. Unfortunately, like many natural language processing techniques, sentiment analysis can show bias against marginalised groups. We illustrate this point by showing how six popular sentiment analysis tools respond to sentences about queer identities, expanding on existing work on gender, ethnicity and disability. We find evidence of bias against several marginalised queer identities, including in the two models from Google and Amazon that seem to have been subject to superficial debiasing. We conclude with guidance on selecting a sentiment analysis tool to minimise the risk of model bias skewing results.

Cheap, Quick, and Rigorous: Artificial Intelligence and the Systematic Literature Review

“…Predictive text and handwriting recognition, web search engines, and machine translation are all based on NLP technologies (Bird et al, 2009). It is also being increasingly adopted in the social sciences (Ungless et al, 2023). The use of MLTs in NLP is useful for researchers as it allows for the automation of tasks, making them more cost and time efficient (Le Glaz et al, 2021).…”

“…Other than SLRs, another research project that may benefit from this method are datasets obtained from semi-structured interviews. Should researchers wish to use MLTs on interview datasets then there are additional biases, such as language, that need to be taken into consideration (Ungless et al, 2023). Utilising the other AI and MLTs outlined in this article, two automated SLRs are currently being conducted on how governance settings can improve the resilience and sustainability of transport and communication infrastructures.…”