ONTOLOGY-BASED SENTIMENT ANALYSIS FOR REAL-TIME PRODUCT REPUTATION MODELING

Abstract

Social Media Platforms Have Become Powerful Channels For User Interaction, Enabling Individuals To Create, Share, And Exchange Information And Media Within Virtual Communities And Networks. E-Commerce Websites Such as Amazon, Ebay, And Cnet Incorporate User-Generated Product Reviews and Rating Systems That Influence Purchasing Decisions. However, Existing Review Systems Are Often Centralized and Static, Limiting Their Responsiveness To Rapidly Changing Consumer Feedback. These Systems Are Vulnerable To False Evaluations And Lack The Adaptability Needed To Reflect Evolving Opinions In Real-Time. This Research Aims To Develop A Dynamic, Ontology-Driven Reputation System That Evolves Over Time To Provide More Accurate And Timely Product Ratings. The Proposed System Is Designed To Compute Dynamic Ratings By Analyzing User Reviews Through A Multi-Layered Process. It Begins With Data Collection From Online Sources, Followed By Sentiment Analysis To Extract Emotional Context And User Opinions From Product Reviews. Subsequently, A Semantic Layer Constructs Semantic Profiles That Capture The Central Ideas Relevant To Decision Support And Reputation Modeling. The Methodology Involves Creating An Adaptive Ontology, Integrating It With A Database, Mapping The Relevant Data Structures, And Generating Semantic Queries To Derive Actionable Insights. By Enabling Real-Time Adaptation and Improved Transparency, The Proposed Approach Supports Both Consumers And Manufacturers In Making Informed Decisions Based On Continuously Updated Reputational Data