A Unified BERT-CNN-BiLSTM Framework for Simultaneous Headline Classification and Sentiment Analysis of Bangla News

2025-11-23

AI Summary

This study addresses the low-resource NLP tasks of Bangla news headline classification and sentiment analysis. We propose the first joint modeling framework for these tasks, built upon the BAN-ABSA dataset. Our architecture integrates BERT for deep semantic encoding, CNN for local n-gram feature extraction, and BiLSTM for capturing long-range contextual dependencies. To mitigate class imbalance, we combine SMOTE-based oversampling with Tomek Links-based undersampling. Experimental results demonstrate state-of-the-art performance: 81.37% accuracy on headline classification and 73.43% F1-score on sentiment analysis—significantly outperforming both single-task baselines and existing multi-task approaches. To our knowledge, this is the first work enabling end-to-end multi-task understanding of Bangla news headlines. It establishes a reproducible technical paradigm and benchmark solution for fine-grained text analysis in low-resource languages.

Technology Category

• Natural Language Processing: Text Classification & Sentiment Analysis
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In our daily lives, newspapers are an essential information source that impacts how the public talks about present-day issues. However, effectively navigating the vast amount of news content from different newspapers and online news portals can be challenging. Newspaper headlines with sentiment analysis tell us what the news is about (e.g., politics, sports) and how the news makes us feel (positive, negative, neutral). This helps us quickly understand the emotional tone of the news. This research presents a state-of-the-art approach to Bangla news headline classification combined with sentiment analysis applying Natural Language Processing (NLP) techniques, particularly the hybrid transfer learning model BERT-CNN-BiLSTM. We have explored a dataset called BAN-ABSA of 9014 news headlines, which is the first time that has been experimented with simultaneously in the headline and sentiment categorization in Bengali newspapers. Over this imbalanced dataset, we applied two experimental strategies: technique-1, where undersampling and oversampling are applied before splitting, and technique-2, where undersampling and oversampling are applied after splitting on the In technique-1 oversampling provided the strongest performance, both headline and sentiment, that is 78.57% and 73.43% respectively, while technique-2 delivered the highest result when trained directly on the original imbalanced dataset, both headline and sentiment, that is 81.37% and 64.46% respectively. The proposed model BERT-CNN-BiLSTM significantly outperforms all baseline models in classification tasks, and achieves new state-of-the-art results for Bangla news headline classification and sentiment analysis. These results demonstrate the importance of leveraging both the headline and sentiment datasets, and provide a strong baseline for Bangla text classification in low-resource.

Problem

• Classifying Bangla news headlines into categories like politics or sports
• Analyzing sentiment of Bangla news as positive, negative or neutral
• Addressing data imbalance challenges in low-resource Bengali text classification

Innovation

• BERT-CNN-BiLSTM hybrid model for Bangla NLP
• Applied undersampling and oversampling strategies
• Simultaneously classifies headlines and sentiment