Elevating sentiment analysis with deep convolutional neural network model facial expression insights

Abstract

In today's data-driven world, the ability to analyze emotional responses is essential. The pressing necessity that drives this study is to revolutionize the field of sentiment analysis by extracting the hidden information from people's facial expressions. It examines people's preferences, worries, and pleasure, revealing their views on many topics. Beyond text-based sentiment analysis, this research adds facial expression-based sentiment analysis into existing systems for tailored recommendations and mental health monitoring. The system emphasizes visual stimuli's emotional influence to improve decision-making, content adaptability, and user experiences. The implementation involves transfer learning with the pre-trained VGG-16 model, which enhances ability to discern intricate emotional cues from facial expressions. Convolutional neural network (CNN) and contextual analysis allow the model to understand users' emotions and provide insights into their thoughts, feelings, and behaviors. To improve emotion recognition reliability and reactivity, this study examines random forest, support vector machine (SVM), and CNN methodologies. The visual geometry group (VGG-16) CNN model outperforms over SVM and random forest classifiers with accuracy of 95%. This study highlights facial expression-based sentiment analysis.