The rapid expansion of social media has led to the generation of massive volumes of data, emphasizing the need to extract valuable insights, categorize information, and predict user sentiments effectively. Text classification, a prominent domain within natural language processing (NLP), focuses on organizing unstructured textual data into sentiment categories to enhance its interpretability. Achieving high accuracy in sentiment categorization calls for refined and efficient text classification techniques. Although Deep Learning models have considerably advanced this field, there remains room for optimization. This study applies the NLP framework to a WhatsApp group dataset to identify sentiment patterns and evaluates five Deep Learning models: Neural Network, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, and Convolutional Neural Network (CNN). Furthermore, it introduces a hybrid CNN–BiLSTM model that integrates feature extraction mechanisms with specific activations, dropouts, filters, kernel sizes, and layered structures to enhance sentiment prediction. The performance of the proposed architecture is benchmarked against existing research. Among individual models, LSTM and BiLSTM achieved the highest accuracy of 81 percent, while the proposed hybrid model attained an improved accuracy of 88 percent on the same dataset, demonstrating superior effectiveness in sentiment classification.
