benheubl.github.io

Data Dico

I am a data journalist and developer. The Good, the Bad and the Ugly: The truth about the female adult model industry. An investigation into the global state of the female model industry triggered an analysis of 9,687 female model profiles and 1,101 modeling agencies. The sea. How to apply face recognition API technology to data journalism with R and python. Predicting who’s is to win the biggest skateboarding contest in the world? Building a model to spot how unique Hillary Clinton really is.

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I am a data journalist and developer. The Good, the Bad and the Ugly: The truth about the female adult model industry. An investigation into the global state of the female model industry triggered an analysis of 9,687 female model profiles and 1,101 modeling agencies. The sea. How to apply face recognition API technology to data journalism with R and python. Predicting who’s is to win the biggest skateboarding contest in the world? Building a model to spot how unique Hillary Clinton really is.

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Data Dico | benheubl.github.io Reviews

https://benheubl.github.io

I am a data journalist and developer. The Good, the Bad and the Ugly: The truth about the female adult model industry. An investigation into the global state of the female model industry triggered an analysis of 9,687 female model profiles and 1,101 modeling agencies. The sea. How to apply face recognition API technology to data journalism with R and python. Predicting who’s is to win the biggest skateboarding contest in the world? Building a model to spot how unique Hillary Clinton really is.

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benheubl.github.io

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Data Dico - Data Dico

https://benheubl.github.io/splash-page

Data Dico means Data speaks or Data explains . This is my data science and journalism blog. Photo credit: Allie Moh. Let the data speak. I use this blog as an innovation lab for new data stories and novel data journalism approaches. Machine learning, new technology and explorative data analysis shall be the core of these posts. All ideas and thoughts shared on this blog are my own. Cherry picked: Data journalism posts @TheEconomist:. US convention - sentiment analysis. Where does foreign aid go? 2016 Pow...

2

Building a model to spot how unique Hillary Clinton really is - Data Dico

https://benheubl.github.io/machine%20learning/navie-bayes

I am a data journalist and developer. Building a model to spot how unique Hillary Clinton really is. How unique is Hillary Clinton’s style? What does her speeches tell us about her uniqueness? In this post I built several Naive Bayes models, trained them on Hillary’s 2016 campaign speeches and applied them on other remarks, tweets and text corpuses. The results is Interesting, and presents another journalistic use-case for machine learning. Naive Bayes for text analysis. The approach has some hooks. ...

3

Predicting who’s is to win the biggest skateboarding contest in the world? - Data Dico

https://benheubl.github.io/machine%20learning/sls-win

I am a data journalist and developer. Predicting who’s is to win the biggest skateboarding contest in the world? What is the strategy to win the word’s biggest Skateboarding event this year, the 2016 SLS Nike SB Super Crown World Championship: A combination of run and best trick skills. An analysis on previous events and scores, with a data-driven judgement on who might have the best chances to win. Street League 2013: Nyjah Huston. This Sunday, the biggest street skateboarding competition will take plac...

4

Hillary Clinton and her strategy to speak - Data Dico

https://benheubl.github.io/data%20analysis/strategy-to-speak

I am a data journalist and developer. Hillary Clinton and her strategy to speak. Text analysis in R on speeches is one way to find some new untold stories in the presidential election discussion. In this post we will concentrate on Hillary Clinton and her strategy to speak. I’m in, and I’m in to win: 2008 vs. 2016. To end up with a relative reliable sentiment line, we will use the tidytext package. Remove all the stop words. All sentiment standard All. All sentiment standard All. After the conventions, i...

5

How to apply face recognition API technology to data journalism with R and python - Data Dico

https://benheubl.github.io/data%20analysis/fr

I am a data journalist and developer. How to apply face recognition API technology to data journalism with R and python. The Microsoft Emotion API is based on state of the art research from Microsoft Research in computer vision and is based on a Deep Convolutional Neural Network model trained to classify the facial expressions of people in videos and images. This is an attempt to explain how to apply the API for data-driven reporting. The Debates are a goldmine for emotion research. Insert your key here'.

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Measuring Gobbledygook – data science ish

http://juliasilge.com/blog/Gobbledygook

A Blog by Julia Silge. November 25, 2016. In learning more about text mining. One of the most commonly used ways to measure readability is a SMOG. Grade, which stands for Simple Measure of Gobbledygook . It may have a silly (SILLY WONDERFUL) name, but it is often considered the gold standard of readability formulas and performs well in many contexts. We calculate a SMOG score using the formula. To compare the readability of several texts. Getting some texts to analyze. Let’s use the gutenbergr package.

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Data Dico

I am a data journalist and developer. The Good, the Bad and the Ugly: The truth about the female adult model industry. An investigation into the global state of the female model industry triggered an analysis of 9,687 female model profiles and 1,101 modeling agencies. The sea. How to apply face recognition API technology to data journalism with R and python. Predicting who’s is to win the biggest skateboarding contest in the world? Building a model to spot how unique Hillary Clinton really is.

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