Nonlinear Deep Learning Framework for Precise Sentiment Classification in Textual Data

The study presents the Nonlinear Deep Learning Framework for Precise Sentiment Classification (NPSC), focusing on improving sentiment analysis in text data. The model addresses the challenge of capturing complex and hidden sentiment patterns using a combination of BERT embeddings, Bidirectional LSTM (BiLSTM), Convolutional Neural Networks (CNN), and an attention mechanism. This setup helps in identifying subtle relationships within text, enabling more precise sentiment predictions. NPSC tackles common issues faced by existing models that struggle with detailed text analysis. Using the IMDB movie review dataset, the approach relies on BERT to convert words into dense vectors, BiLSTM for capturing context from both directions, and CNN to extract key local features. An attention layer highlights important words, refining the sentiment detection process. The integration of these components in a nonlinear fusion layer allows for enhanced feature interaction, leading to accurate classification through a final softmax layer. The findings show that NPSC performs better across metrics like accuracy, precision, recall, and F1-score. Ablation studies reveal the impact of each component, demonstrating their role in enhancing the model’s effectiveness. Error analysis shows how NPSC manages mixed sentiments and challenging text patterns. The study highlights NPSC’s potential as a reliable method for sentiment analysis, contributing to better handling of complex textual data.