Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models
This addresses the issue of emotional bias in news reporting for media outlets and the public, though it is incremental as it builds on existing perturbation and prompt-based techniques.
The paper tackled the problem of sentiment manipulation in news text by reducing polarity while preserving semantics, using adversarial perturbation and ChatGPT-based methods, achieving competitive performance with minimal modifications in experiments and human evaluations.
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.