A key question of collective social behavior is related to the influence of mass media on public opinion. Different approaches have been developed to address quantitatively this issue, ranging from field experiments to mathematical models. In this work we propose a combination of tools involving natural language processing and time series analysis. We compare selected features of mass media news articles with measurable manifestation of public opinion. We apply our analysis to news articles belonging to the 2016 US presidential campaign. We compare variations in polls (as a proxy of public opinion) with changes in the connotation of the news (sentiment) or in the agenda (topics) of a selected group of media outlets. Our results suggest that the sentiment content by itself is not enough to understand the differences in polls, but the combination of topics coverage and sentiment content provides an useful insight of the context in which public opinion varies. The methodology emplo...

中文翻译：

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使用自然语言处理和时间序列分析来分析大众媒体的影响

集体社会行为的一个关键问题与大众传媒对民意的影响有关。从现场实验到数学模型，已经开发出不同的方法来定量解决此问题。在这项工作中，我们提出了涉及自然语言处理和时间序列分析的工具组合。我们将大众传媒新闻文章的精选功能与可测量的舆论表现进行比较。我们将分析应用于属于2016年美国总统大选的新闻。我们将民意测验的变化（作为舆论的代理）与选定媒体组的新闻（情感）或议程（主题）内涵的变化进行比较。我们的结果表明，情感内容本身不足以理解民意测验的差异，但是主题覆盖范围和情感内容的结合提供了对舆论变化背景的有用洞察。方法论应用