Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models (2023)

Abstract: 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 promptbased 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 contextaware 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 promptbased methods, offering a promising solution to foster more objective news reporting and combat emotional language bias in the media.

## introduction news media plays a crucial role in building public opinion on different socio-political issues and events by providing information regarding relevant facts and events. in an ideal case, the news outlets should provide the readers with correct and objective information without any bias or slant. however, news writers intentionally or unintentionally insert their own prejudice or perspectives that reflect how they view reality and what they assume to be the truth in the news broadcasts. while ## conclusion our research aims to reduce sentiment polarity in news reporting using advanced techniques like adversarial attack-based perturbations and large language models such as chatgpt with prompt engineering, striving for more balanced and impartial news narratives. looking forward, we are exploring avenues for improving efficiency in news sentiment mitigation, including automating the identification of news aspects within sentences. we also aim to address implicit sentiment and expand our techniques to cover a wider range of languages and domains, particularly in low-resource language contexts, with the ultimate goal of advancing news reporting towards greater equity and impartiality.