Sentiment analysis of content is highly essential for myriad natural language processing tasks. Particularly, as the movies are often created on the basis of public opinions, reviews of people have gained much attention, and analyzing sentiments has also become a crucial and demanding task. The unique characteristics of this data, such as the length of text, spelling mistakes, and abbreviations, necessitate a non-conventional method and additional stages for sentiment analysis in such an environment. To do so, this paper conducted two different word embedding models, namely GloVe and Word2Vec, for vectorization. In this study, Bidirectional Gated Recurrent Unit was employed, since there were two polarities, including positive and negative. Then, it was optimized by the Enhanced Human Evolutionary Optimization (EHEO) algorithm, hence improving the hyperparameters. The findings showed that using GloVe, the Bi-GRU/EHEO model achieved 97.26% for precision, 96.37% for recall, 97.42% for accuracy, and 96.30% for F1-score. With Word2Vec, the suggested model attained 98.54% for precision, 97.75% for recall, 97.54% for accuracy, and 97.63% for F1-score. These model were compared with other models like GRU that accomplished the precision, recall, accuracy, and F1-score values of 89.24, 90.14, 89.57, and 89.68 for Glove as well the values of 89.67, 90.18, 90.75, and 89.41 for Word2Vec; and Bi-GRU that accomplished the values of 90.13, 90.47, 90.71, and 90.30 for Glove, as well as the values of 90.31, 90.76, 90.67, and 90.53 for Word2Vec. The suggested sentiment analysis approaches demonstrated much potential to be used in real-world applications, like customer feedback evaluation, political opinion analysis, and social media sentiment analysis. By using these models' high efficiency and accuracy, the approaches could have offered some practical solutions for diverse industries to forecast trends, enhance decision-making procedures, and examine textual data.