This visualizations shows the positivity and negativity in yelp reviews. The size of the data points corresponds to the number of words in the review. The sentiment is scored using the NRC lexicon created by Saif M. Mohammad and based on implementation by Matthew Jockers.
Josiah Davis josiahdavis
josiahdavis
/ model.py
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August 23, 2017 17:29
Updating the [census canned estimator sample](https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/census/estimator/trainer) to include key value from input data
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josiahdavis
/ .DS_Store
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October 30, 2015 22:29
— forked from mbostock/.block
Usefulness in Yelp Reviews
josiahdavis
/ .DS_Store
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October 3, 2015 22:10
What words do people use to describe their coffee shop experience?
josiahdavis
/ README.md
Last active
September 26, 2015 01:15
— forked from mbostock/.block
Common Machine Learning Algorithms
Forked from Mike Bostock's Example. Displays common machine learning algorithms.
Not bad, apparently.
This chart plots the average emtional valence contained within Yelp reviews for coffee shops across 8 emotions, four positive emotions on the right, and four negative emotions on the left. Interestingly, Starbucks seems to provide a less emotionally rich experience than going to other coffee shops. Possibly, this is due to the fact that Starbucks customers dissproportionally dilute their coffee with cream and sugar, and thus dilute their coffee-drinking experience. The greatest joy seems to be coming from the Other category, possibly a testament to the romance often associated with Independent Coffee shops. Apparently, the most anticipated place is Krispy Kreme. It makes sense, people likely eat donuts much less than they drink coffee, so when they go, it's a specical event.
This chart is built using the the radar-chart-d3 plugin created Alvaro Graves. The emotional valence was estimated using the [syuzhet R package](
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