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 and provide an
unprecedented, publicly available dataset with technical and fundamental data,
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
comprises of daily entries from January 2018 to December 2022 for 8 different
companies and Dow Jones Index as a whole. Holistic Fundamental and Technical
data is provided training ready for Model learning and deployment. The
predictive power of deep learning models is highly determined by the training
data provided. This dataset would be of benefit for research globally
incorporating qualitative intelligence for stock market forecasting. The
dataset is made available at this https URL

本研究将机器学习应用于金融领域，尤其是股票市场预测。研究通过合作数字股票数据和定性文本数据，提供了一个包含来自新闻档案、电视新闻字幕、广播节目文字转录、推特、日常金融报纸等的 140 多万条文本数据的数据集，该数据集可用于股票市场预测的全球研究中。