This paper addresses the challenge of aspect-level sentiment classification, a fine-grained task in sentiment analysis that provides more detailed and comprehensive results. The authors propose an Attention-based Long Short-Term Memory (AT-LSTM) network to capture the relationship between sentence content and specific aspects, such as *taste* or *service*. The attention mechanism allows the model to focus on different parts of a sentence when different aspects are considered, improving the accuracy of sentiment polarity prediction. The proposed model is evaluated on the SemEval 2014 dataset, demonstrating superior performance compared to baseline models like LSTM, TD-LSTM, and TC-LSTM. The paper also discusses the importance of aspect embeddings and their integration into the LSTM architecture to enhance the model's ability to capture relevant information. Experimental results show that the attention-based approach effectively captures the key parts of sentences, leading to better performance in aspect-level sentiment classification.This paper addresses the challenge of aspect-level sentiment classification, a fine-grained task in sentiment analysis that provides more detailed and comprehensive results. The authors propose an Attention-based Long Short-Term Memory (AT-LSTM) network to capture the relationship between sentence content and specific aspects, such as *taste* or *service*. The attention mechanism allows the model to focus on different parts of a sentence when different aspects are considered, improving the accuracy of sentiment polarity prediction. The proposed model is evaluated on the SemEval 2014 dataset, demonstrating superior performance compared to baseline models like LSTM, TD-LSTM, and TC-LSTM. The paper also discusses the importance of aspect embeddings and their integration into the LSTM architecture to enhance the model's ability to capture relevant information. Experimental results show that the attention-based approach effectively captures the key parts of sentences, leading to better performance in aspect-level sentiment classification.