Deep learning-based sentiment analysis of customer reviews using bidirectional LSTM

In the digital era, online customer reviews play a crucial role in influencing purchasing decisions and shaping business strategies. However, the exponential growth of user-generated textual data makes manual analysis impractical. This study proposes a deep learning-based approach for automatic sentiment classification of customer reviews using a Bidirectional Long Short-Term Memory (BiLSTM) model.
The research focuses on binary sentiment classification, categorizing reviews as positive or negative. A comprehensive Natural Language Processing (NLP) pipeline is developed, including text preprocessing, tokenization, sequence padding, and word embedding. The BiLSTM model is designed to capture contextual dependencies from both forward and backward directions, enabling improved understanding of textual sentiment.
To address real-world challenges such as noisy data and class imbalance, techniques like stop-word removal, label encoding, class weighting, dropout, and early stopping are applied. The model is evaluated using multiple metrics including accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed model achieves high accuracy and robust performance on unseen data.
The performance of the proposed model is rigorously evaluated using multiple evaluation metrics, including accuracy, precision, recall, and F1-score, to ensure a comprehensive assessment of its effectiveness. Particular emphasis is placed on the F1-score, as it provides a balanced measure of model performance in scenarios involving imbalanced datasets. Experimental results demonstrate that the BiLSTM-based model achieves high classification accuracy and exhibits strong robustness when tested on unseen data, outperforming several traditional machine learning approaches.
This study highlights the effectiveness of deep learning techniques in sentiment analysis and provides a scalable solution for real-world applications in e-commerce and customer feedback analysis.