  Predicting future prices of a stock is an arduous task to perform. However,
incorporating additional elements can significantly improve our predictions,
rather than relying solely on a stock's historical price data to forecast its
future price. Studies have demonstrated that investor sentiment, which is
impacted by daily news about the company, can have a significant impact on
stock price swings. There are numerous sources from which we can get this
information, but they are cluttered with a lot of noise, making it difficult to
accurately extract the sentiments from them. Hence the focus of our research is
to design an efficient system to capture the sentiments from the news about the
NITY50 stocks and investigate how much the financial news sentiment of these
stocks are affecting their prices over a period of time. This paper presents a
robust data collection and preprocessing framework to create a news database
for a timeline of around 3.7 years, consisting of almost half a million news
articles. We also capture the stock price information for this timeline and
create multiple time series data, that include the sentiment scores from
various sections of the article, calculated using different sentiment
libraries. Based on this, we fit several LSTM models to forecast the stock
prices, with and without using the sentiment scores as features and compare
their performances.
