The Dark Side of AI Transformers: Sentiment Polarization & the Loss of Business Neutrality by NLP Transformers
This highlights a critical problem for businesses relying on NLP sentiment analytics, as it reveals an incremental but harmful side effect of transformer methods.
The paper identifies that transformer-based sentiment analysis models improve accuracy for one sentiment class but cause polarization and loss of neutrality in another, posing reliability issues for industry applications.
The use of Transfer Learning & Transformers has steadily improved accuracy and has significantly contributed in solving complex computation problems. However, this transformer led accuracy improvement in Applied AI Analytics specifically in sentiment analytics comes with the dark side. It is observed during experiments that a lot of these improvements in transformer led accuracy of one class of sentiment has been at the cost of polarization of another class of sentiment and the failing of neutrality. This lack of neutrality poses an acute problem in the Applied NLP space, which relies heavily on the computational outputs of sentiment analytics for reliable industry ready tasks.