用Transformer模型分析孟加拉语社交媒体评论，识别公众对犯罪事件的态度变化。
Understanding Public Perception of Crime in Bangladesh: A Transformer-Based Approach with Explainability
- 基于XLM-RoBERTa的模型处理孟加拉语评论，准确率达97%。
- 通过可解释AI定位影响情绪判断的关键词汇和表达。
- 为制定公共政策与犯罪预防提供数据支持，适合社会计算研究者参考。
近年来，社交媒体成为公众表达对刑事事件看法的重要平台，舆论随时间动态演变。本研究通过分类用户生成的评论为正面、负面和中立三类，分析孟加拉国公众对犯罪新闻的感知变化。为此构建了一个包含28,528条孟加拉语社交评论的新数据集。提出基于XLM-RoBERTa Base架构的Transformer模型，在孟加拉语情感分析任务中达到97%的分类准确率，优于现有先进方法。为提升模型可解释性，采用可解释AI技术识别驱动情感分类的关键特征。结果表明，基于Transformer的模型在低资源语言如孟加拉语上具有显著效果，具备提取可行动洞察的能力，有助于支持公共政策制定与犯罪预防策略。
原文摘要 · Abstract (English)
In recent years, social media platforms have become prominent spaces for individuals to express their opinions on ongoing events, including criminal incidents. As a result, public sentiment can shift dynamically over time. This study investigates the evolving public perception of crime-related news by classifying user-generated comments into three categories: positive, negative, and neutral. A newly curated dataset comprising 28,528 Bangla-language social media comments was developed for this purpose. We propose a transformer-based model utilizing the XLM-RoBERTa Base architecture, which achieves a classification accuracy of 97%, outperforming existing state-of-the-art methods in Bangla sentiment analysis. To enhance model interpretability, explainable AI technique is employed to identify the most influential features driving sentiment classification. The results underscore the effectiveness of transformer-based models in processing low-resource languages such as Bengali and demonstrate their potential to extract actionable insights that can support public policy formulation and crime prevention strategies.
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