Implementing the Predictive Model of Heuristic Algorithm-assisted Cascaded LSTM for Business Decision-making using Social Media Data

The evolution of social networks helps to rapidly spread information among people. From a wide range of unstructured data sources, business intelligence utilizes the information and transforms it into usable information to assist businesses in making effective decisions for increasing productivity and efficiency. The financial impact of the business on the company is analyzed by certain predictive systems. Predictive analytics boosts an organization’s competitiveness and determines the likelihood of a circumstance. The business organization faces issues related to business information and decision-making, such as inadequate planning, unavailability of resources, inability to identify risks and failure. To overcome such issues, a novel adaptive and automated model is proposed for decision-making in business. With the help of data from social media, a decision making process is implemented. For that purpose, the Adaptive Cascaded Long Short-Term Memory (ACasLSTM) is proposed for providing the decisive outcome. For further enhancement, the parameters of LSTM are optimally chosen by using the Squid Game Optimizer (SGO). Finally, results from the validation declare that it achieves a greater performance of making decisions effectively in the business world.