A Hybrid GloVe-BERT Fusion Model with Multi-Level Attention-Based CNN-BiLSTM for Sentiment Analysis

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Gauging public sentiment toward climate policy from information-rich news headlines remains challenging for conventional text classification approaches. Conventional sentiment analysis tools miss contextual subtleties in brief headlines, whereas deep learning models capture the public perception more accurately. The proposed work presents hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) model that uses combination of GloVe and BERT embeddings with an attention layer for sentiment analysis of climate change news headlines. The novelty of this research lies in the use of GloVe and BERT embeddings through a multi-stage fusion strategy and an attention mechanism to enhance text classification performance in a hybrid model. The architecture employs a hierarchical layering approach to fuse static GloVe embeddings with dynamic, contextualized BERT representations through attention modules that enables the network to selectively focus on salient features. To further model complex semantic dependencies, the design incorporates parallel CNN-BiLSTM branches, structured with residual connections and bolstered with additional layers of attention. Evaluated on 1,023 climate-related headlines annotated on a three-point polarity scale, the proposed model achieves an accuracy of 80.47%, outperforming classi- cal baselines (SVM, Naive Bayes, K-NN) and single branch deep networks (CNN:78.63%, BiLSTM:78.36%). The predictive accuracy of the hybrid model is evaluated using a paired t-test to determine whether the difference between models is statistically significant; this is confirmed by rejecting null hypothesis and accepting alternate hypothesis.This study demonstrates that compact, domain-adaptive deep learning models incorporating contextual embeddings and attention mechanisms that can effectively extract sentiment from news headlines, offering scalable, evidence based tools for tracking climate discourse and information policy decisions.