Words that Matter: The Impact of Negative Words on News Sentiment and Stock Market Index
This work addresses the need for better sentiment analysis in financial news for investors and analysts, though it is incremental as it builds on existing methods with a specific dataset.
The study tackled the problem of how negative words in news sentiment analysis affect the South Korean stock market index KOSPI200, finding that an augmented lexicon with 1,000 negative words increased sentiment negativity and more effectively captured market impact compared to the original lexicon.
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.