With the advent of technological marvels such as digitalization around the world, stock market predictions have entered a more technologically advanced era, reviving the old trading model. With the steady growth of market capitalization, stock trading has become an investment hub for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and assist investors in making sound decisions. Advanced trading models enable researchers to predict the market using non-traditional text data from social media platforms. The use of advanced machine learning methods such as textual data analysis and compilation methods has greatly increased the accuracy of prediction. Meanwhile, stock market analysis and forecasting continue to be one of the most challenging research areas due to volatile, volatile, and volatile data. This study describes the design of machine-based learning strategies for predicting the stock market based on the use of a standard framework. In addition, a comprehensive comparative analysis was performed to obtain an indicator of significance. This research can be useful to emerge researchers to understand the basics and developments of this emerging environment, thus continuing further research in promising ways.