2020 Kaleidoscope
Deciphering Election News Patterns [Week 3, 10/18/2019]
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 261 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 10/18/2019, by order of relevance
Topic 1: Donald Trump/Joe Biden (Topic 1 last week)
Explanation: Related to mentions of each candidate in separate instances (respective campaigning) or to topics combining both (Trump versus Biden issue).Topic 2 & Topic 4: Presidential Debate (not present last week)
Explanation: Related to various interventions from democratic candidates during and after CNN debate.Topic 3: Impeachment (same position last week)
Explanation: Appears in the context of the launch of the impeachment inquiry as well as the recent Trump rallies providing relief from the impeachment inquiry.Topic 5: Pete Buttigieg (Topic 4 last week)
Explanation: Related to both the interventions during the presidential debate as well as overall presence on the public scene.Topic 6: Democratic Party (not present last week)
Explanation: Appears in the context of democratic party dynamics, in particular how the party is positioned with respect to various candidates.Topic 7: Chicago Teachers Negotiations (not present last week)
Explanation: Related to the negotiations between the Chicago mayor and the teachers union.Topic 8: Climate Change (Topic 5 last week)
Explanation: Appears in the context of the democratic platform and the Green New Deal.
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 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] [RNN/CNN] [word embeddings] [question answering] [speech recognition] [similarity judgement/constrained embeddings][learning rate]
Using SumUp ranking methodology, the following articles are most representative of the top 8 topics of the week of 10/18/2019:
Emotion Recognition in Conversations with Transfer Learning from Generative Conversation Modeling. Devamanyu Hazarikaa, Soujanya Poriac, Roger Zimmermanna, Rada Mihalceab
This article is mentioned in relation to NLP, word embeddings.Neural Generation for Czech: Data and Baselines. Ondrej Dusek and Filip Jurcıcek
This article is mentioned in relation to NLP, machine translation.Evolution of Transfer Learning in Natural Language Processing. Aditya Malte, Pratik Ratadiya
This article is mentioned in relation to RNN/CNN, learning rate.Evebot: A Deep Learning Based Chatbot System for Campus Psychological Therapy. Junjie Yin, Zixun Chen, Kelai Zhou, Chongyuan Yu
This article is mentioned in relation to RNN/CNN, learning rate.Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora. Marius Cătălin Iordan, Tyler Giallanza, Cameron T. Ellis, Nicole Beckage, Jonathan D. Cohen
This article is mentioned in relation to word embeddings, similarity judgement.
All of the other articles published during the period under review are individually less relevant to the top topics.
