This paper provides a comprehensive survey of deep learning applications in sentiment analysis. Deep learning, which involves artificial neural networks with multiple layers, has become a powerful technique for learning complex representations of data. Sentiment analysis, the computational study of opinions, sentiments, and attitudes towards entities, has seen significant growth in recent years, driven by the availability of large volumes of opinionated data from social media and online forums. The paper first provides an overview of deep learning and then surveys its current applications in sentiment analysis. Deep learning models, such as neural networks, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and attention mechanisms, have been widely applied in sentiment analysis. These models can effectively capture complex patterns in text data and have shown superior performance compared to traditional methods. Word embeddings, such as Word2Vec and GloVe, are used to represent words as dense vectors, capturing semantic and syntactic relationships. Autoencoders are used for feature learning, while CNNs and RNNs are used for extracting local and sequential features, respectively. LSTM networks are particularly effective for capturing long-term dependencies in text. Attention mechanisms allow models to focus on relevant parts of the input when making predictions. The paper also discusses various sentiment analysis tasks, including document-level sentiment classification, sentence-level sentiment classification, and aspect-level sentiment analysis. Document-level sentiment classification involves determining the overall sentiment of a text, while sentence-level classification determines the sentiment of individual sentences. Aspect-level sentiment analysis focuses on extracting and summarizing opinions about specific aspects of entities. The paper reviews various deep learning approaches for these tasks, including CNNs, RNNs, LSTM networks, and attention mechanisms. It also discusses the use of memory networks and recursive neural networks for sentiment analysis. Overall, deep learning has shown great potential in improving the accuracy and effectiveness of sentiment analysis in various applications.This paper provides a comprehensive survey of deep learning applications in sentiment analysis. Deep learning, which involves artificial neural networks with multiple layers, has become a powerful technique for learning complex representations of data. Sentiment analysis, the computational study of opinions, sentiments, and attitudes towards entities, has seen significant growth in recent years, driven by the availability of large volumes of opinionated data from social media and online forums. The paper first provides an overview of deep learning and then surveys its current applications in sentiment analysis. Deep learning models, such as neural networks, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and attention mechanisms, have been widely applied in sentiment analysis. These models can effectively capture complex patterns in text data and have shown superior performance compared to traditional methods. Word embeddings, such as Word2Vec and GloVe, are used to represent words as dense vectors, capturing semantic and syntactic relationships. Autoencoders are used for feature learning, while CNNs and RNNs are used for extracting local and sequential features, respectively. LSTM networks are particularly effective for capturing long-term dependencies in text. Attention mechanisms allow models to focus on relevant parts of the input when making predictions. The paper also discusses various sentiment analysis tasks, including document-level sentiment classification, sentence-level sentiment classification, and aspect-level sentiment analysis. Document-level sentiment classification involves determining the overall sentiment of a text, while sentence-level classification determines the sentiment of individual sentences. Aspect-level sentiment analysis focuses on extracting and summarizing opinions about specific aspects of entities. The paper reviews various deep learning approaches for these tasks, including CNNs, RNNs, LSTM networks, and attention mechanisms. It also discusses the use of memory networks and recursive neural networks for sentiment analysis. Overall, deep learning has shown great potential in improving the accuracy and effectiveness of sentiment analysis in various applications.