Sentiment-Driven Business Intelligence: AI-Based Framework for Strategic Content and Customer Engagement Optimization

Abstract

In the modern digital economy, consumer sentiment is the key to proper brand communication and strategic decision-making. The present paper outlines the proposal of an AI-based sentiment intelligence system that helps to increase the content curation, brand positioning, and customer interaction in business intelligence systems. The system proposed incorporates ensemble learning methods which incorporate the use of logistic regression, random forest and deep sentiment embeddings to learn both syntactic and contextual information within the large textual data. Using the heterogeneous data sources, including posts on social media, reviews, and feedbacks, the model determines behavioral trends resulting from sentiment, which form the foundation of adaptive business strategies. The experimental experiments prove that the ensemble architecture is strong in multi-domain applications since the predictive accuracy is significantly higher than the baseline classifiers (31%). In addition to technical development, the paper shows the significance of sentiment-informed analytics in leading to innovation management, the quality of communication assurance, and strategic content optimization. The results develop a connection between artificial intelligence and strategic business intelligence, which helps develop a new paradigm of data-driven and sentiment-conscious decision-making in the enterprise.