Sentiment analysis is essential in identifying the sentiment of reviewers toward any product or situation and the sequential learning models have been perennial in sentiment analysis due to their ability to handle sequential dependencies, but unidirectional sequential models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) often overlook contextual dependencies of textual data. Here bidirectional variants of these sequential models are much better at capturing contextual dependencies. This study investigates the effectiveness of these bidirectional models, specifically bidirectional LSTM (Bi-LSTM) and bidirectional GRU (Bi-GRU) by using text datasets from IMDB and Wikipedia. In this study, the efficacy of these models is also evaluated for tasks like sentiment analysis and text classification. The bidirectional variant of LSTM and GRU models enhances classification accuracy and computational efficiency. The experimental results showcase that Bi-LSTM achieves an accuracy of 87.7% for sentiment analysis while Bi-GRU achieves an accuracy of 87.5%. This study contributes to the growing knowledge of applying advanced sequential networks in natural language processing.