Abstract
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.
Abstract (translated)
近年来，社交媒体平台已成为个人表达对当前事件（包括犯罪事件）看法的重要空间。因此，公众情绪会随着时间的推移而动态变化。本研究通过将用户生成的评论分类为三类：积极、消极和中立，来探讨与犯罪相关的新闻报道中的公众认知演变。为此目的开发了一个新整理的数据集，包含28,528条孟加拉语社交媒体评论。 我们提出了一种基于变压器架构（使用XLM-RoBERTa Base）的模型，在孟加拉语情感分析中实现了97%的分类准确率，超越了现有最先进的方法。为了提高模型可解释性，采用了可解释的人工智能技术来识别驱动情绪分类的关键特征。研究结果强调了基于Transformer的模型在处理包括孟加拉语在内的低资源语言中的有效性，并展示了它们提取可用于支持公共政策制定和犯罪预防策略的实用信息的能力。
URL
https://arxiv.org/abs/2507.21234
