Abstract

Social media platforms have generated massive visual content streams, where sentiment interpretation remains crucial for adaptive marketing and decision making. Problem: existing models have struggled to integrate spatial visual cues with temporal dependencies, which has limited forecasting reliability under noisy and dynamic environments. Method: this study has proposed an Evolutionary Fuzzy CNN-BiLSTM Fusion (EFCBF) framework that has combined convolutional feature extraction, fuzzy logic-based uncertainty handling, and BiLSTM temporal modeling optimized through an evolutionary algorithm. Results: the proposed system has demonstrated improved sentiment classification stability and forecasting accuracy across benchmark social media datasets, outperforming baseline deep learning and hybrid models in precision, recall, and F1-score metrics. The fuzzy inference layer has reduced ambiguity in visual sentiment interpretation, while the evolutionary optimization has enhanced parameter selection efficiency and convergence behavior. The integrated CNN-BiLSTM architecture has captured both local spatial patterns and sequential dependencies effectively, which has strengthened predictive consistency under diverse content distributions. The proposed model achieves 91.2% accuracy, 90.3% precision, 89.9% recall, 90.1% F1-score, and 0.94 AUC-ROC, which outperform CNN-LSTM Hybrid, Fuzzy Logic Classifier, and Evolutionary CNN across all evaluation steps. The fuzzy layer improves uncertainty handling, while evolutionary optimization enhances parameter stability and convergence behavior. The CNN-BiLSTM fusion effectively captures spatial and temporal dependencies in social media image sequences.

Authors

D.K. Mohanty¹, R. Manoharan²
Government B.Ed. Training College Kalinga, India¹, Apollo Engineering College, India²

Keywords

Visual Sentiment Analysis, CNN-BiLSTM, Fuzzy Logic System, Evolutionary Optimization, Social Media Analytics