Applications that generate valuable sentiment-rich data, such as movie reviews, social media monitoring, and customer feedback evaluation, depend heavily on sentiment analysis. However, because sentiment is contextual and subtle, it can be difficult to convey successfully in writing and frequently necessitates an awareness of long-term word relationships. Because they can process sequential input, recurrent neural network (RNN) types like LSTM and GRU are frequently utilized for such tasks. In this work, we assess how well LSTMs and GRUs execute sentiment analysis, particularly when applied to movie reviews. According to the results, LSTMs perform better than GRUs at managing long-term dependencies, which makes them more appropriate for jobs where a sentence’s sentiment depends on a comprehension of word relationships across the text. When important contextual information was dispersed over lengthy sequences or when complicated sentence structures were involved, LSTMs showed improved accuracy. Although GRUs are quicker to train and more computationally efficient, their simplicity makes it difficult for them to accurately grasp long-range dependencies. These findings provide important information for future NLP model selection and optimization, showing that LSTMs are more resilient for sentiment analysis tasks requiring greater contextual awareness, even while GRUs may be preferable in settings that prioritize computing speed.