A Context Driven Knowledge Formalization to Enhance the Effectiveness of Sentiment Analysis in E-commerce Operations

E-commerce has become an unavoidable force reshaping competition and consumer behavior across industries. Businesses increasingly depend on sentiment analysis to extract consumer intelligence from vast amounts of online reviews, social media, and digital interactions. However, existing models often ignore contextual information, limiting their ability to capture nuanced meanings in consumer sentiment. To address this gap, we propose a contextual ontology-based theoretical model architecture that integrates sentiment analysis with knowledge graph construction. Contextual ontology enables the formal representation of semantic meaning as it varies by context, thus reducing ambiguity in textual data interpretation. While simple algorithms may suffice for a consumer with straightforward preferences, such as price as an overriding criterion, only a contextual ontology can capture the complex, multi-criteria behaviors of more sophisticated consumers. Our theory framework demonstrates how consumer intelligence can be enriched by combining lexicon-based sentiment classification with ontology-driven context modeling, creating more robust insights for e-commerce decision-making. The knowledge graph in our architecture captures multi-dimensional relationships and dynamically updates as interactions evolve, allowing managers to filter context-induced noise and achieve precision market segmentation. By integrating context variables with non-context variables, businesses can move from generic opinion mining to context-aware sentiment analysis that optimizes strategic resources and enhances customer lifetime value (CLV). By invoking a behavioral Sensemaking lens, our framework accounts for the complex, identity-driven nuances and multifaceted personality traits that govern sophisticated consumer behaviors. This research contributes to both theory and practice by advancing sentiment analysis methods beyond context-free approaches and by offering e-commerce managers a pragmatic tool for high-velocity consumer intelligence gathering.