The discipline of sentiment classification has gained prominence in the domain of Natural Language Processing (NLP) due to its focus on identifying the overall sentiment conveyed in a given text. Significantly, deep learning models have shown exceptional effectiveness in this particular field. User feedback is of utmost importance in improving the efficacy of recommender systems, since it often encompasses a wide range of emotional data that has the ability to influence the accuracy and precision of suggestions. The information given by users is subjected to analysis using a deep learning model to provide a potential user rating, which may afterwards be used for the purpose of making recommendations. The essay starts by offering a thorough delineation of emotion analysis and expounding upon its many applications. The ensuing discourse centred on the use of recurrent neural networks (RNNs), long short-term memory (LSTM) networks, deep belief networks (DBNs), and hierarchical deep belief networks (HDBNs) within the framework of sentiment analysis.

Sentiment Analysis; RNN; LSTM; HBDN; DBN;