Semantic analysis using deep learning is a key research area in natural language processing that focuses on understanding the meaning, context, and relationships of words, phrases, and sentences in text. Early approaches relied on distributional semantics and word embeddings such as Word2Vec and GloVe to capture semantic similarity, while recent research leverages deep neural networks, including recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and convolutional neural networks (CNNs), for context-aware semantic representation. Transformer-based architectures, such as BERT, RoBERTa, and GPT, have significantly advanced semantic understanding by capturing bidirectional context and complex dependencies in large-scale corpora. Applications of semantic analysis include sentiment analysis, question answering, information retrieval, knowledge graph construction, textual entailment, and machine translation. Current studies also explore multilingual and cross-lingual semantic modeling, domain adaptation, integration with knowledge bases, and explainable AI techniques, establishing deep learning-based semantic analysis as a cornerstone for intelligent natural language understanding systems.