Summary of state-of-the-art models.

Bangla news floods the web, and the need for smarter and more efficient classification techniques is greater than ever. Previous studies mostly focused on traditional models, overlooking the potential of hybrid techniques to handle the ever-growing complex dataset and its linguistic patterns in Bangla to achieve higher accuracy. Addressing the challenge, this study presents a comprehensive approach to classify Bangla news articles into eight distinct categories using various machine learning and deep learning techniques. The use of traditional machine learning algorithms, deep learning architectures, and hybrid models, including novel stacking classifiers, was a part of our experiment. This study utilized a dataset of 118,404 Bangla news articles, applying rigorous feature extraction techniques including TF-IDF vectorization and word2Vec embeddings. Our best-performing model, a stacking meta-classifier combining bidirectional long short-term memory and support vector machine, achieved a remarkable 94% accuracy, leaving all basic models’ performance behind. Also, we provided an in-depth analysis of model performances, including confusion matrices, ROC curves, and error analysis, offering insights into the strengths and limitations of each approach. This research contributes significantly to the field of Bangla natural language processing and demonstrates the efficacy of ensemble methods and deep learning in news classification for low-resource languages.孟加拉语（Bangla）新闻大量充斥于网络空间，学界与业界对更智能、高效的分类技术的需求也达到了前所未有的高度。过往相关研究大多聚焦于传统模型，却忽视了混合技术在处理日益复杂的孟加拉语数据集及其固有语言模式、以实现更高分类精度方面的潜力。针对这一挑战，本研究提出一套完整的分类方案，依托多种机器学习与深度学习技术，将孟加拉语新闻文章划分为八个明确类别。本实验涵盖了传统机器学习算法、深度学习架构以及包括新型堆叠分类器在内的混合模型的构建与测试。本研究采用了包含118404条孟加拉语新闻文章的数据集，并运用了严格的特征提取技术，包括TF-IDF向量化与word2Vec词嵌入。本研究表现最佳的模型是结合双向长短期记忆网络（bidirectional long short-term memory）与支持向量机（support vector machine）的堆叠元分类器，其取得了高达94%的优异分类精度，远超所有基础模型的表现。此外，本研究还对各模型的表现展开了深入分析，涵盖混淆矩阵、ROC曲线与误差分析，为各方法的优势与局限性提供了详实的见解。本研究为孟加拉语自然语言处理领域作出了重要贡献，并证明了集成学习方法与深度学习在低资源语言新闻分类任务中的有效性。