Sentiment Analysis for Social Media Applications: A Deep Learning and Neuro-Symbolic Perspective

Abstract

In the current paper we address the sentiment analysis case of social media by comparing some of the deep learning, hybrid and neuro-symbolic approaches to this specific area. Social media text is distinctive both in form and in area, usually with sentiment in different languages and in an unformatted state that is hard to forecast sentiment when these hierarchical and contextual influences are extremely crucial. The results from the four different datasets (i.e., IMDB, Toxic Comments, CrowdFlower, and Emotion Recognition (ER)) show that these deep architectures (i.e., BiLSTM, GRU, and CNN-LSTM) perform well due to focuses on context, sequential dependencies, and accuracy of classification. However, they face challenges in interpretability and generalizing to multilingual or code-switched situations. To tackle these problems, the research investigates capsule-based and hybrid models such as Capsule-BiLSTM and CRDC. CRDC architecture combines the deep CNN, Bi-RNN, and capsule layers into a single architecture that demonstrates the most powerful system, providing state-of-the-art results (up to 95.48% on ER), at the cost of increased computation. The work is further extended by researching neuro-symbolic integration, with models that blend symbolic reasoning systems (e.g., SenticNet, ConceptNet and emotion lexicons) with deep architectures (e.g., BERT, GPT). These systems exhibit useful potential in use cases where interpretability is more valuable than accuracy, such as possible applications to policy analysis or monitoring hate speech in mental health. As a conclusion, the analysis postulates that CRDC is best for high-accuracy needs while the neuro-symbolic models still provide the best trade-off between explainability and context depth. Future scope includes multimodal sentiment analysis, multilingual enhancement and deployment of lightweight real-time architectures (particularly those based on symbolic augmentation and edge-compatible neural networks). Scalable, ethical and interpretable social media sentiment analysis is on the way with our multi-faceted model.