This paper provides an overview of deep learning and its applications in sentiment analysis. Sentiment analysis, or opinion mining, involves computational methods to understand people's opinions, sentiments, and emotions towards various entities. The field has grown significantly due to the proliferation of social media and the availability of large volumes of opinionated data. Deep learning, a powerful machine learning technique, has emerged as a popular method for sentiment analysis, offering state-of-the-art results. The paper begins by introducing neural networks, explaining their structure and how they learn through multiple layers of nonlinear processing units. It then delves into deep learning, highlighting its recent resurgence due to advancements in computing power, large datasets, and the ability to learn intermediate representations. Key deep learning architectures discussed include word embeddings, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, attention mechanisms, memory networks, and recursive neural networks. Each architecture is described in detail, along with their applications in sentiment analysis. The paper also surveys various sentiment analysis tasks, such as document-level, sentence-level, and aspect-level sentiment classification. For each task, it reviews existing deep learning methods and highlights their strengths and weaknesses. Examples of deep learning models used for these tasks are provided, including CNNs, RNNs, LSTMs, and attention mechanisms. Overall, the paper offers a comprehensive survey of deep learning techniques and their applications in sentiment analysis, providing insights into the latest advancements and future directions in the field.This paper provides an overview of deep learning and its applications in sentiment analysis. Sentiment analysis, or opinion mining, involves computational methods to understand people's opinions, sentiments, and emotions towards various entities. The field has grown significantly due to the proliferation of social media and the availability of large volumes of opinionated data. Deep learning, a powerful machine learning technique, has emerged as a popular method for sentiment analysis, offering state-of-the-art results. The paper begins by introducing neural networks, explaining their structure and how they learn through multiple layers of nonlinear processing units. It then delves into deep learning, highlighting its recent resurgence due to advancements in computing power, large datasets, and the ability to learn intermediate representations. Key deep learning architectures discussed include word embeddings, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, attention mechanisms, memory networks, and recursive neural networks. Each architecture is described in detail, along with their applications in sentiment analysis. The paper also surveys various sentiment analysis tasks, such as document-level, sentence-level, and aspect-level sentiment classification. For each task, it reviews existing deep learning methods and highlights their strengths and weaknesses. Examples of deep learning models used for these tasks are provided, including CNNs, RNNs, LSTMs, and attention mechanisms. Overall, the paper offers a comprehensive survey of deep learning techniques and their applications in sentiment analysis, providing insights into the latest advancements and future directions in the field.