  The application of Machine learning to finance has become a familiar
approach, even more so in stock market forecasting. The stock market is highly
volatile, and huge amounts of data are generated every minute globally. The
extraction of effective intelligence from this data is of critical importance.
However, a collaboration of numerical stock data with qualitative text data can
be a challenging task. In this work, we accomplish this by providing an
unprecedented, publicly available dataset with technical and fundamental data
and sentiment that we gathered from news archives, TV news captions, radio
transcripts, tweets, daily financial newspapers, etc. The text data entries
used for sentiment extraction total more than 1.4 Million. The dataset consists
of daily entries from January 2018 to December 2022 for eight companies
representing diverse industrial sectors and the Dow Jones Industrial Average
(DJIA) as a whole. Holistic Fundamental and Technical data is provided training
ready for Model learning and deployment. Most importantly, the data generated
could be used for incremental online learning with real-time data points
retrieved daily since no stagnant data was utilized. All the data was retired
from APIs or self-designed robust information retrieval technologies with
extremely low latency and zero monetary cost. These adaptable technologies
facilitate data extraction for any stock. Moreover, the utilization of
Spearman's rank correlation over real-time data, linking stock returns with
sentiment analysis has produced noteworthy results for the DJIA and the eight
other stocks, achieving accuracy levels surpassing 60%. The dataset is made
available at https://github.com/batking24/Huge-Stock-Dataset.
