  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.
