News media often shape the public mood not only by what they report but by how they frame it. The same event can appear calm in one outlet and alarming in another, reflecting subtle emotional bias in reporting. Negative or emotionally charged headlines tend to attract more attention and spread faster, which in turn encourages outlets to frame stories in ways that provoke stronger reactions. This research explores that tendency through large-scale emotion analysis of Bengali news. Using zero-shot inference with Gemma-3 4B, we analyzed 300000 Bengali news headlines and their content to identify the dominant emotion and overall tone of each. The findings reveal a clear dominance of negative emotions, particularly anger, fear, and disappointment, and significant variation in how similar stories are emotionally portrayed across outlets. Based on these insights, we propose design ideas for a human-centered news aggregator that visualizes emotional cues and helps readers recognize hidden affective framing in daily news.

翻译：新闻媒体不仅通过报道内容，也通过报道框架来塑造公众情绪。同一事件在一家媒体中可能显得平静，在另一家却令人警觉，这反映了报道中微妙的情感偏见。负面或情绪化的标题往往吸引更多关注并传播更快，进而促使媒体以引发更强反应的方式构建报道。本研究通过对孟加拉语新闻的大规模情感分析来探索这一趋势。利用Gemma-3 4B的零样本推理技术，我们分析了30万条孟加拉语新闻标题及其内容，以识别每条新闻的主导情绪和整体基调。研究结果揭示了负面情绪（尤其是愤怒、恐惧和失望）的明显主导地位，以及不同媒体对相似报道情感呈现方式的显著差异。基于这些发现，我们提出了一种人本新闻聚合器的设计理念，该聚合器可可视化情感线索，帮助读者识别日常新闻中隐藏的情感框架。