Leveraging Artificial Intelligence to Enhance Efficiency and Decision-Making in the Digital Economy

Abstract

Artificial Intelligence (AI) is increasingly used in the digital economy to enhance company intelligence, customer
engagement, operational efficiency, and decision-making. Here, a novel model, namely Intelligent Multiple Social
Network-based Media Analytics with Sentiment Analysis and Susceptible–Infected Model (ITMSSI), is developed. At
the initial stage, the proposed model processes businesstransaction records, consumer interaction data, enterprise digital transformation indicators, and digital platform activities, which are examples of digital economy datasets collected and preprocessed. Latent Dirichlet Allocation (LDA) is used in AI-based topic modeling to find emerging market opportunities, consumer preferences, and business trends. Kernel Density Estimation (KDE) and Standard Deviational Ellipse (SDE) are used to assess data distribution and market activity trends. A Digital Economy Utilization Index (DEUI) is formulated to quantify the effectiveness of the digital economy. To predict the growth of the digital economy and facilitate wise decision-making, an AI-based predictive modeling framework is utilized. The simulation is performed in the Python tool, and the analysis is calculated for network models such as the Twitch social network, the ego-gplus network, the GitHub social network, and the large Twitch network, as well as the sentimental score. This analysis shows the improvement of the proposed ITMSSI model compared with earlier models, such as AICSM, ASGHM, and SIMID.