Semantic analysis of user-generated content, including tweets and comments, is essential for gleaning important patterns and insights in the quickly changing social networking scene. With an emphasis on Recurrent Neural Networks (RNNs), this research investigates how natural language processing (NLP) techniques can improve semantic analysis. By using the sequential nature of RNNs, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures, we aim to accurately capture the contextual relationships found in textual data. To ensure noise reduction and text normalization, our method starts with gathering and preprocessing data from many social networking sites. After that, we use padding and tokenization strategies to get the data ready for model training. Building and training an RNN model using pre-trained word embeddings to improve semantic understanding forms the basis of our research.