In the age of digital marketing, brands constantly seek to understand consumer sentiment and optimize their future brand messaging for maximum impact. This paper explores the potential of lexical analysis and Natural Language Processing (NLP) techniques to extract valuable insights from consumer responses (comments) across multiple campaigns on Meta (formerly Facebook). By analyzing the online narrative brand campaigns and campaign themes within brand advertisements on social media pages, we aim to uncover the subtle emotional undertones that influence consumer perception. Leveraging advanced NLP models, we apply sentiment analysis, word frequency analysis, and semantic analysis to a corpus of contemporary brand campaigns. Our methodology allows us to analyze brand campaigns through a bootstrapped lexicon developed for the Indian diaspora. This enabled us to identify recurring campaign themes used by brands and correlate them with consumer sentiment intensity scores. We also investigate the role of campaign theme, and cultural context in shaping consumer responses and subsequently consumer brand perception. The lexical analysis with granular lexical analysis provides a nuanced understanding of how specific thematic choices impact brand perception. The results of this study highlight the power of lexical analysis in revealing consumer sentiment, enabling brands to fine-tune their future brand messaging strategies. By integrating AI-driven analysis into their marketing workflows, companies can better align their communication with consumer expectations, fostering more authentic and effective brand-consumer relationships. Ultimately, this paper contributes to the growing body of research on AI and marketing, offering a data-driven framework for optimizing brand campaigns and audience engagement through lexical insights.