This study investigates the impact of negative words on sentiment analysis and its effect on the South Korean stock market index, KOSPI200. The research analyzes a dataset of 45,723 South Korean daily economic news articles using Word2Vec, cosine similarity, and an expanded lexicon. The findings suggest that incorporating negative words significantly increases sentiment scores' negativity in news titles, which can affect the stock market index. The study reveals that an augmented sentiment lexicon (Sent1000), including 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 sentiment lexicon (Sent0). The results underscore the importance of considering negative nuances and context when analyzing news content and its potential impact on market dynamics and public opinion.

翻译：本研究探讨了负面词汇对情绪分析的影响及其对韩国股市指数KOSPI200的作用。研究分析了45,723篇韩国每日经济新闻文章的数据集，采用Word2Vec、余弦相似度及扩展词库。结果表明，纳入负面词汇会显著增加新闻标题情绪得分的消极性，进而可能影响股市指数。研究发现，扩充后的情绪词库（Sent1000）包含与“危机”具有高余弦相似度的前1000个负面词汇，相比原始情绪词库（Sent0），能更有效地捕捉新闻情绪对股市指数的影响。这些结果突显了在分析新闻内容及其对市场动态与公众舆论的潜在影响时，考虑负面语义细微差别和语境的重要性。