All posts by Stephanie Kim

An Emotion Recognition API for Analyzing Facial Expressions

Using Emotion Recognition AlgorithmsReading emotional expression is one of the most difficult tasks for humans, let alone computers. Two people looking at the same photo might not agree whether someone is grimacing or grinning. Until recently, computers weren’t much better at the job, either.

Fortunately advances in deep learning has brought us speed, efficiency and accuracy in detecting people’s emotions in photos.

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How to Retrieve Tweets By Keyword and Identify Named Entities

Identifying named entities in tweetsLast week we introduced the named entity recognition algorithm for extracting and categorizing unstructured text.

In this post we’ll show you how to get data from Twitter, clean it with some regex, and then run it through named entity recognition. With the output we get from the algorithm, we can then group the data by the category each named entity is assigned to, and then extract the categories we are interested in.

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Using Named Entity Recognition to Categorize Text Data

Use Named Entity Recognition to Categorize Unstructured TextUnstructured text content is rich with information, but it’s not always easy to find what’s relevant to you.

With the enormous amount of data that comes from social media, email, blogs, news and academic articles, it becomes increasingly hard to extract, categorize, and learn from that information.

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A Fast Way to Scrape Image URLs from Webpages


Let’s say you’ve created an awesome application that colorizes images. Everybody loves it, but some users are getting errors.

You realize they’re trying to pass a URL to a webpage with an image on it, instead of a direct path to the image itself. Your app is expecting a .JPG, or .PNG. Read More…

Introduction to Machine Learning for Developers

Understanding Machine LearningToday’s developers often hear about leveraging machine learning algorithms in order to build more intelligent applications, but many don’t know where to start.

One of the most important aspects of developing smart applications is to understand the underlying machine learning models, even if you aren’t the person building them. Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning. Read More…

Using R to Build a Sentiment Analysis Forecasting Pipeline

Using R to Forecast Sentiment AnalysisTime series forecasting algorithms are a common method for predicting future values based on historical data using sequential data, such as snowfall per hour (anyone ready for snowboarding season?), customer sign-ups per day, or quarterly sales data. In this R recipe, we’ll show how to easily link algorithms together to create a data analysis pipeline for sentiment time series forecasting.

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