Leveraging Convolutional Neural Networks and Sentiment Analysis for Enhanced Brand Awareness Monitoring in Social Media Platforms

Abstract

This research paper explores a novel approach to brand awareness monitoring on social media by integrating Convolutional Neural Networks (CNNs) with sentiment analysis techniques. With the increasing influence of social media on consumer behavior, brands are compelled to monitor their online presence meticulously. Traditional methods often fall short in capturing the dynamic and nuanced nature of social media interactions. This study proposes a two-step analytical framework that capitalizes on the image processing capabilities of CNNs and the textual analysis strength of sentiment analysis algorithms to provide a comprehensive evaluation of brand mentions. Firstly, the framework utilizes CNNs to analyze images associated with brand mentions, extracting relevant features that contribute to brand perception. Secondly, sentiment analysis is applied to the textual content accompanying these images to assess the emotional tone and sentiment trajectory related to the brand. By synthesizing insights from both visual and textual data, the proposed method offers a multidimensional perspective on brand awareness trends. The experimental results, conducted on a dataset comprising diverse social media posts about leading consumer brands, indicate a significant improvement in the accuracy and depth of brand perception analysis compared to conventional methods. This dual-modality approach demonstrates the potential to enhance marketing strategies by providing brands with actionable insights into consumer sentiments and engagement patterns. The implications for marketing practitioners and the future directions for research in social media brand monitoring are also discussed.