AI-Driven Emotional Recognition in Digital Ads: A Novel Approach to Consumer Engagement
DOI:
https://doi.org/10.47363/JMSCM/2023(2)131Keywords:
Facial Expression Analysis, Voice Tone Analysis, Sentiment Analysis, Text Analysis, Computational Advertising, Neuromarketing, Convolutional Neural Networks, Machine Learning, Artificial Intelligence, Digital Marketing, Consumer Engagement, Dataset, MultimodalAbstract
This research aims to comprehensively review the current state of artificial intelligence techniques for emotional recognition and their potential applications in optimizing digital advertising strategies. A systematic literature review was conducted involving searches of academic databases and screening of papers on topics relating to emotion recognition using facial analysis, sentiment analysis, computational advertising, and measuring digital engagement.Key studies developing AI models for multimodal emotion recognition from videos, images, and neurophysiological signals were analyzed. The report finds that while progress has been made in fields like facial emotion detection and sentiment analysis of social media data, limitations remain around data and context. It concludes that further work developing larger datasets, advancing multimodal approaches, and accounting for dynamic contexts could help realize the full benefits of emotion AI for personalized digital marketing.
