Tag Archives: AlchemyAPI

Code Recipe: How to Calculate Twitter Sentiment Using AlchemyAPI with Python

via How to Calculate Twitter Sentiment Using AlchemyAPI With Python | AlchemyAPI.

One of the most common applications of text analysis is sentiment analysis on social media posts, with Twitter being a common data source. Gathering Twitter sentiment data isn’t actually too difficult once you have the right tools. This tutorial will walk you through the steps required to connect to the Twitter API, search for Tweets, and perform sentiment analysis using AlchemyAPI’s sentiment API.

Here, we will show you how to get the data, calculate sentiment, and do some basic analysis and visualization of your results. By the end of this guide you’ll be ready to expand on this simple application to create an extremely powerful solution. While this tutorial uses Python and R, it should be easy to follow a similar methodology to recreate this application in the programming language(s) of your choice.

Build a simple face detection Web App

via Build a simple face detection web app.

Earlier this year, IBM acquired the AlchemyAPI company. AlchemyAPI is a leading provider of scalable cognitive computing and deep learning technology that enables computers to understand language and vision just like human beings. Language-relevant services, such as sentiment analysis, keyword extraction, and relation extraction, can help analyze the content behind sophisticated natural language and add high-level semantic information. Vision-relevant services, such as image tagging and face detection, help us understand a picture’s content and context.

In this tutorial, you learn to build a simple web application that demonstrates the face detection function of the AlchemyAPI service on IBM Bluemix™. The face detection feature can:

  • Study images and determine whether there are faces.
  • Determine the gender and estimated age of the face.
  • Attempt to identify the face, pulling from a corpus of over 60,000 well-known people.

This step-by-step example shows how easy it is to develop a cognitive app on IBM Bluemix with only a few lines of code in PHP.