Traditional English text sentiment analysis models have limitations in handling complex texts and long-term dependency relationships, leading to an impact on the accuracy and deep understanding of sentiment analysis. This article aims to solve the above problems by applying the LSTM (long short-term memory) algorithm. First, the data is cleaned and preprocessed, including regular expressions, word segmentation, stop word filtering, and stem extraction. Next, a sentiment lexicon is utilized to annotat. Word2Vec and TF-IDF are combined for feature vectorization, and the positional encoding is applied. An LSTM network structure is then constructed. After training optimization, the model achieves an accuracy of up to 90% on the test set, outperforming other models. The LSTM algorithm effectively solves the limitations of traditional models and achieves higher accuracy. In summary, the application of LSTM algorithm in English text sentiment analysis models can effectively address existing limitations and make them more accurate.