2020 Kaleidoscope

SumUp Analytics
Oct 26 · 4 min read

SumUp Analytics

Oct 25· 4 min read

Deciphering Election News Patterns [Week 4, 10/25/2019]

sumup.ai

This newsletter is a weekly publication showcasing two simple applications of SumUp’s platform: an analysis of 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 281 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 10/25/2019, by order of relevance

Topic 1: Donald Trump/Joe Biden
Explanation: Related to mentions of each politician including campaign but also impeachment related themes

Topic 2: Presidential candidates: Pete Buttigieg/Bernie Sanders
Explanation: Related to various interventions from both candidates on the campaign trail

Topic 3: Iowa, New Hampshire, South Carolina
Explanation: Mentioned in the context of upcoming primaries and various candidate campaigning

Topic 4: Hillary Clinton /Tulsi Gabbard
Explanation: Related to the comments made by Hillary Clinton n Tulsi Gabbard during the week

Topic 5: Pete Buttigieg/Wealth tax
Explanation: Pete Buttigieg appears both for a wealth tax proposal and women empowerment legislation proposals.

Topic 6: Robert Mueller/criminal investigation
Explanation: Mentioned in context of the newly become criminal investigation opened by DOJ regarding aspects of Mueller report.

Topic 7: Second and third-tier democratic candidate
Explanation: Mentioned in various contexts, such as their respective opinions on impeachment

Topic 8: DNC
Explanation: Mentioned in relation to next democratic debate and qualifying criteria

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. In order to facilitate that analysis, we added the actual Nucleus analysis to the annex, providing further measure of sentiment attached to each of these topics, or a summary explanation of each topic.

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 10/18/2019

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 11th October 2019, the following 8 key topics are extracted:

[Natural Language Processing] [Machine Translation][Speech recognition][similarity/pre-training] [question answering/knowledge graph] [learning rate] [language learning/transfer learning] [reinforcement learning]

Using SumUp ranking methodology, the following articles are most representative of the top 8 topics of the week of 10/25/2019:

Controlling Utterance Length in NMT-based Word Segmentation with Attention Pierre Godard, Laurent Besacier, Francois Yvon

[Natural Language Processing] [Machine Translation]

Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model Yu Chen, Lingfei Wu, Mohammed J. Zaki

[Machine Translation][reinforcement learning]

Localization of Fake News Detection via Multitask Transfer Learning Jan Christian Blaise Cruz, Julianne Agatha Tan, and Charibeth Cheng

[learning rate] [language learning/transfer learning]

Transformer-based acoustic modeling for hybrid speech recognition Yongqiang Wang, Abdelrahman Mohamed, Duc Le, Chunxi Liu, Alex Xiao, Jay Mahadeokar, Hongzhao Huang, Andros Tjandra, Xiaohui Zhang, Frank Zhang, Christian Fuege, Geoffrey Zweig, Michael L. Seltzer,

[Machine Translation][Speech recognition]

Rethinking Exposure Bias In Language Modeling Yifan Xu, Kening Zhang, Haoyu Dong, Yuezhou Sun, Wenlong Zhao & Zhuowen Tu

[language learning/transfer learning] [reinforcement learning]

Robust Neural Machine Translation for Clean and Noisy Speech Transcripts Mattia Di Gangi, Robert Enyedi, Alessandra Brusadin, Marcello Federico

[Natural Language Processing] [Machine Translation]

All of the other articles published during the period under review are individually less relevant to the top topics.

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