The increasing reliance on digital platforms has transformed the tourism and hospitality industry into a data-rich environment where both structured business intelligence (BI) data and unstructured customer opinion data flow continuously. However, existing studies often treat these streams in isolation, limiting their ability to provide a holistic understanding of customer behavior. This paper proposes a data-centric framework that integrates BI with sentiment analysis to enhance marketing intelligence in hospitality and tourism. The framework combines structured data such as booking histories, demographics, and spending patterns with unstructured opinion data drawn from customer reviews, social media, and travel blogs. The methodology incorporates data cleaning, sentiment scoring, and hybrid feature selection, followed by supervised learning models including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) networks. Recommendation systems are further enhanced by combining collaborative filtering with sentiment-enriched content-based filtering. The framework is evaluated through benchmark datasets and real-world hospitality reviews using a ten-fold cross-validation scheme. The results demonstrate that the hybrid approach significantly improves sentiment classification accuracy (up to 92% with LSTM), reduces error in recommendation systems (RMSE = 0.72; MAE = 0.54), and yields measurable business benefits. Simulated campaigns achieved a 12% increase in booking conversion rates, an 8% rise in guest satisfaction, and a 10% improvement in customer retention compared to BI-only baselines. The study offers theoretical contributions by bridging behavioral and perceptual dimensions of customer intelligence, and practical implications by providing hospitality firms with a scalable framework for real-time, customer-centric decision-making. Limitations integrating BI with sentiment analysis as a pathway toward smart, adaptive marketing systems in the tourism and hospitality sector.