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Detecting Shifts in Public Opinion: A Big Data Study of Global News Content

  • Saatviga Sudhahar
  • Nello Cristianini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11191)

Abstract

Rapid changes in public opinion have been observed in recent years about a number of issues, and some have attributed them to the emergence of a global online media sphere [1, 2]. Being able to monitor the global media sphere, for any sign of change, is an important task in politics, marketing and media analysis. Particularly interesting are sudden changes in the amount of attention and sentiment about an issue, and their temporal and geographic variations. In order to automatically monitor media content, to discover possible changes, we need to be able to access sentiment across various languages, and specifically for given entities or issues. We present a comparative study of sentiment in news content across several languages, assembling a new multilingual corpus and demonstrating that it is possible to detect variations in sentiment through machine translation. Then we apply the method on a number of real case studies, comparing changes in media coverage about Weinstein, Trump and Russia in the US, UK and some other EU countries.

Keywords

Media content monitoring Public opinion Sentiment analysis Machine translation Big data 

Notes

Acknowledgements

Saatviga Sudhahar and Nello Cristianini are supported by the ERC Advanced Grant “ThinkBig awarded to NC.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of BristolBristolUK

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