  In today's media landscape, where news outlets play a pivotal role in shaping
public opinion, it is imperative to address the issue of sentiment manipulation
within news text. News writers often inject their own biases and emotional
language, which can distort the objectivity of reporting. This paper introduces
a novel approach to tackle this problem by reducing the polarity of latent
sentiments in news content. Drawing inspiration from adversarial attack-based
sentence perturbation techniques and a prompt based method using ChatGPT, we
employ transformation constraints to modify sentences while preserving their
core semantics. Using three perturbation methods: replacement, insertion, and
deletion coupled with a context-aware masked language model, we aim to maximize
the desired sentiment score for targeted news aspects through a beam search
algorithm. Our experiments and human evaluations demonstrate the effectiveness
of these two models in achieving reduced sentiment polarity with minimal
modifications while maintaining textual similarity, fluency, and grammatical
correctness. Comparative analysis confirms the competitive performance of the
adversarial attack based perturbation methods and prompt-based methods,
offering a promising solution to foster more objective news reporting and
combat emotional language bias in the media.
