The Dark Side of AI Transformers: Sentiment Polarization & the Loss of Business Neutrality by NLP Transformers

TL;DR

This paper investigates the negative side effects of AI Transformers in sentiment analysis, revealing that they cause sentiment polarization and undermine neutrality, which impacts industry applications.

Contribution

It uncovers the polarization and neutrality loss caused by transformers in sentiment analytics, highlighting a critical challenge in applied NLP.

Findings

Transformers improve accuracy but cause sentiment polarization.

Sentiment neutrality is compromised in transformer-based models.

Polarization affects reliability of NLP applications.

Abstract

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.