What matters, context or sentiment?: Analysing the influence of news in U.S. elections using Natural Language Processing
A key question in the analysis of collective social behaviour is related to know if and how mass media can influence public opinion. In this paper, we explore quantitatively the relation between a specific manifestation of public opinion and the intention to vote a given candidate, with the information related to the candidates in the Mass Media. We analyse the political news articles related to the US presidential campaign during the year 2016, using techniques of natural language processing. We applied recursive deep models for semantic composition over sentiment treebank to be able to detect the sentiment 1 ar X iv :1 90 9. 08 09 5v 1 [ cs .S I] 6 S ep 2 01 9 of sentences, and topic detection methods in order to characterise how media outlets get involved in the election coverage. The results of the analysis were compared to the outcomes of political polls in order to know which of these two aspects of the information have more influence and if there exists any causal relationship between them. Our results suggest that the sentiment content of the news by itself is not enough to understand the differences in the polls between both candidates (validating the popular quote “There is no such thing as bad publicity”) but the topic coverage distribution does and the sentiment content discriminated by topic is consistent with these results. In particular, we found that one topic (news related to Clinton’s email scandal) is the more relevant in this analysis because it correlates negatively and shows a significant causal relation with the difference between Clinton and Trump in the polls and also explain the negative values of sentiment analysis in Fox News coverage.