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A BERT–LSTM–Attention Framework for Robust Multi-Class Sentiment Analysis on Twitter Data

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Publicado en:Systems vol. 13, no. 11 (2025), p. 964-983
Autor principal: Zhang, Xinyu
Otros Autores: Liu, Yang, Zhang, Tianhui, Hou Lingmin, Liu, Xianchen, Guo Zhen, Mulati Aliya
Publicado:
MDPI AG
Materias:
Data analysis
Data augmentation
Accuracy
Deep learning
Datasets
Classification
Artificial intelligence
Sentiment analysis
Social networks
Neural networks
Ablation
Natural language processing
Multilingualism
Robustness (mathematics)
Machine learning
Annotations
Real time
Emojis
Digital media
Semantics
Acceso en línea:Citation/Abstract
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Resumen:This paper proposes a hybrid deep learning model for robust and interpretable sentiment classification of Twitter data. The model integrates Bidirectional Encoder Representations from Transformers (BERT)-based contextual embeddings, a Bidirectional Long Short-Term Memory (BiLSTM) network, and a custom attention mechanism to classify tweets into four sentiment categories: Positive, Negative, Neutral, and Irrelevant. Addressing the challenges of noisy and multilingual social media content, the model incorporates a comprehensive preprocessing pipeline and data augmentation strategies including back-translation and synonym replacement. An ablation study demonstrates that combining BERT with BiLSTM improves the model’s sensitivity to sequence dependencies, while the attention mechanism enhances both classification accuracy and interpretability. Empirical results show that the proposed model outperforms BERT-only and BERT+BiLSTM baselines, achieving F1-scores (F1) above 0.94 across all sentiment classes. Attention weight visualizations further reveal the model’s ability to focus on sentiment-bearing tokens, providing transparency in decision-making. The proposed framework is well-suited for deployment in real-time sentiment monitoring systems and offers a scalable solution for multilingual and multi-class sentiment analysis in dynamic social media environments. We also include a focused characterization of the dataset via an Exploratory Data Analysis in the Methods section.
ISSN:2079-8954
DOI:10.3390/systems13110964
Fuente:Advanced Technologies & Aerospace Database