Sentiment Analysis in Python with TextBlob

Welcome to the eleventh blog of 52 Technologies in 2016 blog series. If you are following this series then you would have probably noticed that I already wrote week 11 blog on tweet deduplication. I was not happy with the content so I decide to write another blog for week 11.

In week 11, I decided to spend time to learn about text processing using the Python programming language. We will only focus on Sentiment Analysis in this blog. I have written about sentiment analysis multiple times in last few years. We learnt how to do sentiment analysis in Scala using Stanford CoreNLP in week 3 blog. Sentiment analysis gives you the power to mine emotions in text. This can help you build awesome applications that understand human behavior. Few years back, I built an application that helped me decide if I should watch a movie or not by doing sentiment analysis on social media data for a movie. There are many possible applications of Sentiment analysis like understanding customer sentiment for a product by analysis of reviews.

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Gatling: The Ultimate Load Testing Tools for Programmers

Welcome to the tenth blog of 52 Technologies in 2016 blog series. Gatling is a high performance open source load testing tool built on top of Scala, Netty, and Akka. It is a next generation, modern load testing tools very different from existing tools like Apache JMeter. Load testing is conducted to understand behavior of an application under load. You put load on the application by simulating users and measure its response time to understand how application will behave under both normal and anticipated peak load conditions.

Gatling can be used to load test your HTTP server. HTTP is not the only protocol that one can load test with Gatling. Gatling also has inbuilt support for Web Socket and JMS protocols. You can extend Gatling to support your protocol of choice.

Load testing is often neglected by most software teams resulting in poor understanding of their application performance characteristics. These days most software teams take unit testing and functional testing seriously but still they ignore load testing. They write unit tests, integration tests, and functional tests and integrate them in their software build. I think part of the reason developer still don’t write load tests has to do with the fact that most load testing tools are GUI based so you can’t code your load tests. They allow you to export your load test as XML.

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Realtime People Counter with Google’s Cloud Vision API and RxJava

Welcome to the ninth blog of 52 Technologies in 2016 blog series. Recently, Google released Cloud Vision API that enables developers to incorporate image recognition in their applications. Image Recognition allow developers to build applications that can understand content of images. Google’s Cloud Vision API is very powerful and support following features:

  1. Image categorization: The API can help classify images into categories. You can build powerful applications like Google Photos that do automatic categorization.
  2. Inappropriate content detection: The API can detect inappropriate content in an image like nudity, violence, etc. It uses Google Safe search capabilities underneath.
  3. Emotion detection: This allows you to detect happy, sad or moderate emotions in an image.
  4. Retrieve text from the image: This allows you to extract text in multiple languages from the images.
  5. Logo detection: It can help you identify product logos within an image.

There are many possible applications that you can build using this powerful API. In this tutorial, we will learn how to build a realtime people counter. The application will subscribe to a twitter stream for a topic and would return number of people found in each image. We can then use this data to get advanced statistic like number of people in a time frame using RxJava buffer capabilities.

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CoreOS for Application Developers

Welcome to eighth week of 52 Technologies in 2016 blog series. This week we will learn about CoreOS, an Open source Linux distribution built to run and manage highly scalable and fault tolerant systems. It is designed to docker and rocket containers. When I started learning about CoreOS, I was overwhelmed by its complexity and different components that you have to know and interact with like etcd, systemd, fleet, Flannel. I am not an Ops guy so CoreOS documentation and many tutorials that I found on the web didn’t clicked with me. The goal of this tutorial is to help application developers understand why they should care about CoreOS and show them how to work with CoreOS cluster running on top of Amazon EC2.

What is CoreOS?

According to CoreOS website, CoreOS is a Linux for Massive Server Deployments. This means it is not a general purpose Linux distro that you can use as your development workspace instead, you will use it to run and your applications at scale.

Built on Chrome OS, CoreOS is a lean and mean operating system that runs minimal Linux. When you limit your OS to the bare minimal i.e. just openssl, ssh, linux kernel, gcc then you need a mechanism to run package and run applications that you want to use. CoreOS does not even has a package manager like yum or Apt. CoreOS is very different from other Linux distributions as it is centered around containers. Linux Containers is an operating-system-level virtualization environment for running multiple isolated Linux systems (containers) on a single Linux control host. CoreOS uses containers to run and manage applications services. You package application along with its dependencies within a container that can be run on a single or multiple CoreOS machines. CoreOS supports both Docker and Rocket containers.

Docker is the poster child of containers. In November 2013, I first learnt and wrote about Docker. Docker is a set of toolset geared around containers. Docker clicked with everyone and overnight became the tool that everyone wanted to learn and introduce in their organization. One reason Docker became popular very quickly is its approachability to an average developer. To use Docker, you don’t have to know Linux internals and work with complicated tools.

CoreOS developers claim that it is 40% more efficient in RAM usage than an average linux installation.

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Hugo: A Modern WebSite Engine That Just Works

This week I decided to take a break from Scala and scratch my own itch my building an online bookshelf using Hugo. Hugo is a static site generator written in Go programming language. You can use it for building modern static websites. Static site generator takes your content files written in a markup language like Markdown, apply layouts you have defined, and generate static HTML files that can be delivered to the user. Static websites are nothing new, they date back to the first ever website in human history. We started with static websites, then moved to dynamic websites, and finally we are moving back to static websites for use-cases where it make sense. Most common use-cases for static websites are blogs, product documentation, help guides, tutorials, online portfolio or resume.

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Building A Lightweight Scala REST API Client with OkHttp

Welcome to the sixth blog of 52-technologies-in-2016 blog series. In this blog, we will learn how to write Scala REST API client for Medium’s REST API using OkHttp library. REST APIs have become a standard method of communication between two devices over a network. Most applications expose their REST API that developers can use to get work with an application programmatically. For example, if I have to build a realtime opinion mining application then I can use Twitter or Facebook REST APIs to get hold of their data and build my application. To work with an application REST APIs, you either can write your own client or you can use one of the language specific client provided by the application. Last few weeks, I have started using Medium for posting non-technical blogs. Medium is a blog publishing platform created by Twitter co-founder Evan Williams. Evan Williams is the same guy who earlier created Blogger, which was bought by Google in 2003.

Medium exposed their REST API to the external world last year. The API is simple and allows you to do operations like submitting a post, getting details of the authenticated user, getting publications for a user, etc. You can read about Medium API documentation in their Github repository. Medium officially provides REST API clients for Node.js, Python, and Go programming languages. I couldn’t find Scala client for Medium REST API so I decided to write my own client using OkHttp.

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Python json object_hook

Today, I was building a REST client for one of the REST server applications using Python. I decided to use Python requests library for writing my REST API client. Requests is a very easy to use library that you can use to quickly bootstrap your REST API client. Writing REST client for REST endpoints was a matter of an hour. This REST API client will be used from our custom Jython(JVM implementation of Python) REPL. REST API has only two endpoints that return JSON objects. Response of first endpoint was fed to the second endpoint. I was returning the JSON response as Python dictionary. User can change values of the first response and pass it to the second API call. In Python, you work with dictionary as shown below. Continue reading