Sentiment analysis, a subfield of natural language processing (NLP), has garnered significant attention in recent years due to its wide range of applications in various domains, such as social media monitoring, customer feedback analysis, and market research. In this review paper, we focus on the application of neural network models for sentiment analysis tasks. Neural network models have shown remarkable performance in capturing intricate patterns and nuances in textual data, making them particularly suitable for sentiment analysis tasks. We provide a comprehensive overview of neural network architectures employed in sentiment analysis, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models. Additionally, we discuss various preprocessing techniques, feature representations, and training strategies commonly used in conjunction with neural network models for sentiment analysis. Furthermore, we present a comparative analysis of different neural network architectures in terms of their effectiveness, efficiency, and scalability for sentiment analysis tasks. Finally, we outline current challenges and future directions in leveraging neural network models for sentiment analysis, aiming to provide insights and guidance for researchers and practitioners in the field of NLP.