Deciphering Election News Patterns [Week 8, 12/13/2019]
Dec 13· 3 min read
This newsletter is a weekly publication showcasing two simple applications of SumUp’s platform: an analysis of the news pertaining to the 2020 elections and a technical review of research published on computational linguistics.
2020 Election: Last week’s news coverage
What topics are most emphasized across the mainstream media’s coverage of the 2020 presidential election?
We ran 181 articles from 13 news feeds (in annex) through the SumUp platform. All information used is freely and easily available. We didn’t apply any filters. Keep in mind that these results are only representative of the 13 news sources listed at the bottom of this article.
This blog is not expressing an opinion but rather highlights the most relevant topics in the recent news coverage.
SumUp 2020 key topics on 12/13/2019, by order of relevance
Topic 1: Joe Biden/ Elizabeth Warren
Explanation: Related to general campaign trail events and various pollings, specifically the upcoming caucus/primary states
Topic 2: Trump Impeachment/Pensacola shooting
Explanation: Related to large number of mentions of both news items
Topic 3: Republican and Democratic primaries/caucuses
Explanation: Related to the proximity of the elections
Topic 4: Saudi sympathies/impeachment turn off
Explanation: Essentially related to two prominent news flows: the impact of the Pensacola events on Saudi sympathies and the turn off effect of the impeachment proceedings in some states
Topic 5: Pete Buttigieg
Explanation: Related to mentions of Pete Buttigieg campaigning efforts and various recent polls
Topic 6: Impeachment Proceedings/President Trump support
Explanation: Combined mentions of impeachment proceeding and votes along party lines with Republican support for Donald Trump
Topic 7: Health care
Explanation: Frequent subject on the campaign trail
Topic 8: Impeachment Inquiry
Explanation: Related to the omni-presence of the theme of impeachment throughout the news.
These topics are only representative of the key subjects appearing in the sources of information reviewed by Nucleus. A more refined interpretation, in terms of content or order of importance, is left to the reader and could easily be pursued with the Nucleus platform.
Annex 1 Election 2020 news sources:
https://www.theguardian.com/us-news/us-elections-2020 https://fivethirtyeight.com/politics/elections/ https://www.chicagotribune.com/politics/elections/ https://www.politico.com/news/2020-elections https://www.cnn.com/specials/politics/2020-election-coverage https://time.com/5675691/2020-election-candidates-website-disability-accessibility/ https://www.newyorker.com/tag/2020-election https://www.foxnews.com/category/politics/2020-presidential-election https://www.usatoday.com/search/?q=2020%20democratic%20elections https://www.washingtonexaminer.com/tag/2020-elections https://www.washingtontimes.com/elections/ https://www.vox.com/2020-presidential-election https://www.breitbart.com/tag/2020-election/
Computational Linguistic Research Week of 12/13/2019
Leveraging its text analytics platform, SumUp has designed a simple method to rank recent research publications, purely based on their relation to the top topics extracted from these publications. Creating a corpus composed of all recent publications (all publications published on arxiv.org over the last week), Nucleus extracts 8 key topics representative of that research corpus. Nucleus then identifies the top documents related to these topics. SumUp ranks articles, according to the number of times each article is mentioned in the 8 extracted topics.
Using the corpus composed of articles published during the week of the 13th December 2019, the following 8 key topics are extracted:
[NLP/machine translation][RNN/CNN][transfer learning/word embedding][deep learning/ reinforcement learning][language model/ transformer][question answering][sentiment analysis/social media][relation extraction/domain adaptation]
Using SumUp ranking methodology, the following articles are most representative of the top 8 topics of the week of 12/13/2019:
Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model Hamid Mohammadi, Seyed Hossein Khasteh
[NLP/machine translation][RNN/CNN][deep learning/ reinforcement learning][language model/ transformer]
Machine Translation Evaluation Meets Community Question Answering Francisco Guzman, Lluıs Marquez and Preslav Nakov
[NLP/machine translation][RNN/CNN][transfer learning/word embedding][question answering]