This paper introduces an innovative strategy for cross-domain sentiment analysis utilizing decision trees, a versatile tool for classification tasks. Sentiment analysis, pivotal for deciphering subjective information within textual data, holds significance in market analysis, customer feedback analysis, and brand management. Our method entails extracting sentiment-oriented features from text data, employing methodologies like bag-of-words or word embedding, and training decision tree models on labeled datasets spanning various domains. Diverging from traditional approaches reliant solely on domain-specific attributes, our framework integrates domain-independent features to facilitate knowledge transfer across domains. Through exhaustive experimentation across datasets encompassing product reviews, social media content, and news articles, we showcase the efficacy of our method in achieving competitive performance vis-à-vis contemporary techniques. Furthermore, we investigate the influence of domain adaptation techniques such as domain adversarial training and transfer learning on model efficacy, shedding light on the interpretability of sentiment models and enabling more precise sentiment prediction in real-world scenarios spanning diverse domains.