<![CDATA[The rapid growth of social media platforms has transformed how consumers express their opinions, making sentiment analysis a critical tool for understanding consumer behavior. This research explores the use of Artificial Intelligence (AI) in sentiment analysis, specifically through Natural Language Processing (NLP) techniques, to analyze consumer sentiment on social media platforms such as Twitter and Instagram. By employing sentiment classification models, including BERT (Bidirectional Encoder Representations from Transformers) and Logistic Regression with TF-IDF, the study aims to uncover patterns in consumer sentiment and provide insights to businesses for developing effective marketing strategies. The results demonstrate that BERT outperforms Logistic Regression, offering higher accuracy, precision, recall, and F1-score in sentiment classification. Additionally, sentiment trend analysis highlights how consumer opinions fluctuate over time in response to marketing campaigns, while sentiment distribution analysis provides an overview of the general attitude toward products. This study offers a comprehensive AI-driven framework for businesses to improve customer satisfaction, optimize marketing efforts, and enhance brand loyalty through real-time sentiment insights.]]>
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