Sentiment Analysis of Financial News and its Impact on the Stock Market

In recent years, there has been significant interest and innovation in combining sentiment analysis techniques with financial markets. This study provides a concise review of the literature on sentiment analysis applied to stock market news and its subsequent impact on market behavior. The research methodology involved extracting data from stock market news sources and using sentiment analysis, specifically the NLTK Vader tool, to categorize this information. The focus was on understanding how sentiment analysis influences stock market dynamics. Additionally, the study included the use of machine learning models to further explore these relationships. The findings contribute to our comprehension of how sentiment analysis can classify stock market data and its influence on market behavior, highlighting the potential applications of sentiment analysis in financial decision-making processes. This research emphasizes the importance of combining sentiment analysis techniques with machine learning algorithms like Random Forest, ARIMA and LSTM to gain insights into the complex interplay between news sentiment and stock market movements.