This paper proposes a hybrid model (HBCNLS) for sentiment analysis that combines the strengths of multiple machine learning approaches. The model consists of a convolutional neural network (CNN) for feature extraction, a long short-term memory (LSTM) network for capturing sequential dependencies, and a fully connected layer for classification on movie review dataset. We evaluate the performance of the HBCNLS on the IMDb movie review dataset and compare it to other state-of-the-art models, including BERT. Our results show that the hybrid model outperforms the other models in terms of accuracy, precision, and recall, demonstrating the effectiveness of the hybrid approach. The research work also compares the performance of BERT, a pre-trained transformer model, with long short-term memory (LSTM) networks and convolutional neural networks (CNNs) for the task of sentiment analysis on a movie review dataset.
