This study investigates the impact of biased language, specifically 'Words that Wound,' on sentiment analysis in a dataset of 45,379 South Korean daily economic news articles. Using Word2Vec, cosine similarity, and an expanded lexicon, we analyzed the influence of these words on news titles' sentiment scores. Our findings reveal that incorporating biased language significantly amplifies sentiment scores' intensity, particularly negativity. The research examines the effect of heightened negativity in news titles on the KOSPI200 index using linear regression and sentiment analysis. Results indicate that the augmented sentiment lexicon (Sent1000), which includes the top 1,000 negative words with high cosine similarity to 'Crisis,' more effectively captures the impact of news sentiment on the stock market index than the original KNU sentiment lexicon (Sent0). The ARDL model and Impulse Response Function (IRF) analyses disclose that Sent1000 has a stronger and more persistent impact on KOSPI200 compared to Sent0. These findings emphasize the importance of understanding language's role in shaping market dynamics and investor sentiment, particularly the impact of negatively biased language on stock market indices. The study highlights the need for considering context and linguistic nuances when analyzing news content and its potential effects on public opinion and market dynamics.

翻译：本研究考察了偏见性语言（具体而言即“伤人之词”）对情感分析的影响，数据集包含45,379篇韩国每日经济新闻文章。使用Word2Vec、余弦相似度及扩展词库，我们分析了这些词对新闻标题情感得分的影响。研究发现表明，纳入偏见性语言会显著放大情感得分的强度，尤其是消极情绪。本研究通过线性回归和情感分析，检验了新闻标题中加剧的消极情绪对KOSPI200指数的影响。结果表明，相比原始KNU情感词库（Sent0），包含与“危机”余弦相似度最高的前1000个负面词的增强情感词库（Sent1000）能更有效地捕捉新闻情感对股票市场指数的影响。ARDL模型与脉冲响应函数（IRF）分析显示，与Sent0相比，Sent1000对KOSPI200具有更强且更持久的影响。这些发现强调了理解语言在塑造市场动态和投资者情绪中的作用的重要性，尤其是负面偏见性语言对股票市场指数的影响。本研究凸显了在分析新闻内容及其对公众舆论和市场动态潜在影响时，需考虑语境和语言细微差别的必要性。