Optimising Machine Learning Integration in Real-Time Text Analytics Platforms: Technical Approaches and Performance Criteria

Abstract

The article investigates the integration of machine learning into real-time platforms for analysing text streams. The relevance of the topic is driven by the growing volume of unstructured textual data and the need for its prompt and accurate processing to support decision-making in such fields as media monitoring, cybersecurity, finance, and healthcare. The effectiveness of such platforms is shown to depend on the adaptability of algorithms, analysis accuracy, scalability, and transparency of results. Special attention is paid to the technical aspects of implementation, including distributed architecture, streaming data processing, optimisation of computing resources, and integration of explainable models. The purpose of the article is to study the possibilities of integrating machine learning algorithms into real-time platforms for analysing text streams, in particular, to develop approaches to improving the efficiency of data processing, ensuring their transparency and adaptability in a changing information environment. To achieve this goal, the study applies a combination of literature analysis, comparative evaluation of existing algorithms, and an experimental assessment of technical solutions. The findings indicate that the main challenges of integration include the computational complexity of deep models, scalability constraints, and delays in data stream processing. It has been shown that the use of distributed computing technologies, hardware accelerators (GPU/TPU), and online learning mechanisms significantly improves the performance of such platforms. The application of adaptive algorithms capable of real-time parameter updates increases analysis accuracy under unstable data conditions. The study concludes that integrating machine learning into real-time systems enhances the speed, reliability, and scalability of text analytics. Further research should focus on developing universal multilingual platforms that combine energy efficiency, modularity, and high analytical performance.