Leveraging Convolutional Neural Networks and Sentiment Analysis for Enhanced AI-Driven Brand Awareness Monitoring

Abstract

This research paper explores the integration of convolutional neural networks (CNNs) and sentiment analysis to advance AI-driven brand awareness monitoring. In the current dynamic market landscape, understanding consumer perception and brand sentiment is crucial for maintaining competitiveness. Traditional methods of brand monitoring often fall short in processing the vast amounts of data generated across digital platforms. This study proposes a novel framework utilizing CNNs to efficiently analyze visual content and sentiment analysis to interpret textual data related to brand mentions. By leveraging large-scale datasets from social media, the proposed model is trained to identify brand logos in images and perform sentiment analysis on accompanying text, thereby providing a comprehensive measure of brand perception and awareness. The results demonstrate that the combined approach significantly enhances accuracy and depth of insights compared to existing methods, achieving a sentiment classification accuracy of 91% and a logo detection precision of 94%. The paper further discusses the implications for marketers, recommending the adoption of such advanced AI techniques to proactively respond to consumer sentiment and manage brand reputation in real-time. Future research directions include the integration of multimodal data sources and the refinement of algorithms for even greater predictive capabilities.