AI-POWERED SENTIMENT ANALYSIS IN DIGITAL MARKETING: A REVIEW OF CUSTOMER FEEDBACK LOOPS IN IT SERVICES

Abstract

This systematic review critically examines the evolving role of AI-powered sentiment analysis in optimizing digital marketing strategies, with a specific focus on its application within customer feedback loops in IT service environments. In the era of data-driven marketing, the ability to decode consumer emotions from unstructured textual sources—such as social media, product reviews, helpdesk transcripts, and chat logs—has become increasingly valuable for enhancing personalization, engagement, and service responsiveness. Adhering to the PRISMA 2020 methodology, this review rigorously analyzed 87 peer-reviewed articles published between 2010 and 2024, encompassing diverse disciplines including artificial intelligence, natural language processing, marketing analytics, and service operations. The findings reveal that while traditional stochastic models like Support Vector Machines remain widely used due to their computational efficiency and interpretability, deep learning architectures—particularly CNNs, LSTMs, and GRUs—have demonstrated superior performance in managing complex, context-rich sentiment patterns. Moreover, transformer-based models such as BERT and RoBERTa have emerged as state-of-the-art tools, excelling in multilingual sentiment interpretation and capturing nuanced emotional dynamics in long-form or domain-specific feedback. The integration of these models into customer feedback loops has enabled real-time marketing decision-making, automated customer relationship management, and sentiment-driven content optimization. However, the review also identifies key gaps, notably the underutilization of internal enterprise data sources and the lack of comprehensive adoption of explainable AI practices. Increasing scrutiny under data protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) has further underscored the need for transparency, user consent, and ethical handling of inferred emotional data. Overall, this review contributes to the growing body of literature by offering a comprehensive evaluation of current technologies, identifying operational challenges, and highlighting the need for ethically aligned and context-aware sentiment analytics frameworks in digital marketing ecosystems, particularly within the IT services sector.