This repository contains an application which is built to demonstrate as an example of Apache Storm distributed framework by performing sentiment analysis of tweets originating from U.S. in real-time. This Topology retrieves tweets originating from US and computes the sentiment scores of States [based on tweets] continuously i.e. till the time the topology is killed. User has to explicitly kill the topology for exiting the application.
Apache Storm is an open source distributed real-time computation system, developed at BackType by Nathan Marz and team. It has been open sourced by Twitter [post BackType acquisition] in August, 2011. And Storm became a top level project in Apache on 29th September, 2014.
This application has been developed and tested initially with Storm v0.8.2 on Windows 7 in local mode; and was eventually updated and tested with Storm v0.9.3 on 01st January, 2015. Application may or may not work with earlier or later versions than Storm v0.9.3.
This application has been tested in:
- Local mode on a CentOS virtual machine and even on Microsoft Windows 7 machine.
- Cluster mode on a private cluster and also on Amazon EC2 environment of 4 machines and 5 machines respectively; with all the machines in private cluster running Ubuntu while EC2 environment machines were powered by CentOS.
- Recent update to Apache Storm v0.9.3 has not been tested in a Cluster mode.
You might also be interested in checking out extension of this repo for Twitter sentiment of various states of US using D3.js Choropleth Map and Highcharts Columncharts on StormTweetsSentimentD3Viz and also a similar project for UK Twitter Sentiment on StormTweetsSentimentD3UKViz.
- Application retrieves tweets from Twitter stream (using Twitter4J).
- It analyses sentiments of the tweets from US.
- There are three different objects within a tweet that we can use to determine it’s origin. This application tries to find the location using all the three options and prioritizes location received in the following order [high to low]:
- The coordinates object.
- The place object.
- The user object.
- For reverse geocoding, this application uses Bing Maps API.
- For more information and sign up, please check Getting Started with Bing Maps.
- Please note that you would need Windows Live account for signing up for Bing Maps API key.
- Also, please consider opting for Basic Plan for Bing Maps API, as that is better for our usage. As of 18th June, 2013, limit is 50k requests for 24 hours in Basic Plan.
- I chose Bing Maps and not Google Maps since Google Maps is too restrictive for our usage, as it has a limit of only 2500 requests per day, which is too small a threshold for our work.
- This application uses AFINN, which contains a list of word with its pre-computed sentiment score.
- These words are used to determine sentiment of the tweets retrieved using Twitter Streaming API.
- While processing, after every 30 seconds [configurable], the application logs the sentiment values of the states to the console and simultaneously to a log file using Logback.
- By understanding sentiment values, we can get the most happiest state of US and most unhappiest state as well.
- As discussed in the initial note above, this idea has been extended further to create real-time Twitter sentiment visualization of 2 countries in a Choropleth Map. Please check the following projects for more info on them.
- StormTweetsSentimentD3Viz -- Repo for US Twitter Sentiment Visualization
- StormTweetsSentimentD3UKViz -- Repo for UK Twitter Sentiment Visualization
- As discussed in the initial note above, this idea has been extended further to create real-time Twitter sentiment visualization of 2 countries in a Choropleth Map. Please check the following projects for more info on them.
- This codebase has been updated with decent comments, wherever required.
- Also this project has been made compatible with both Eclipse IDE and IntelliJ IDEA. Import the project in your favorite IDE [which has Maven plugin installed] and you can quickly follow the code.
- Please check the
config.propertiesand add your own values and complete the integration of Twitter API to your application by looking at your values from Twitter Developer Page.
- If you did not create a Twitter App before, then please create a new Twitter App where you will get all the required values of
config.propertiesafresh and then populate them here without any mistake.
- If you did not create a Twitter App before, then please create a new Twitter App where you will get all the required values of
- Also please add the value of Bing Maps API Key to
config.properties, as that will be used for getting the reverse geocode location using Latitude and Longitude.
- If you do not have Bing Maps API Key, please check Getting Started with Bing Maps for signup and other information.
- If you do not have Bing Maps API Key, please check Getting Started with Bing Maps for signup and other information.
- And finally please check [but do not modify] the
AFINN-111.txtfile to see the pre-computed sentiment scores of ~2500 words / phrases.- For more info on AFINN, please check its
AFINN-README.txtand also check his paper.
- For more info on AFINN, please check its
- Apache Storm v0.9.3
- Twitter4J v4.0.2
- Google Guava v18.0
- Logback v1.1.2
- Jackson v1.9.13
Also, please check pom.xml for more information on the various other dependencies of the project.
This project uses Apache Maven to build and run the topology.
You need the following on your machine:
- Oracle JDK >= 1.8.x
- Apache Maven >= 3.2.3
- Clone this repo and import as an existing Maven project to either Eclipse IDE or IntelliJ IDEA.
- This application uses Google Guava for making life simple while using Collections and other generic stuff.
- This application also uses Jackson for unmarshalling the JSON response got from Bing Maps API.
- Requires ZooKeeper, etc installed and configured in case of executing this project in distributed mode i.e. Storm Cluster.
- Follow the steps on Storm Wiki for more details on setting up a Storm Cluster.
- Follow the steps on Storm Wiki for more details on setting up a Storm Cluster.
Rest of the required frameworks and libraries are downloaded by Maven as required in the build process, the first time the Maven build is invoked.
To build and run this topology, you must use Java 1.8.
Local mode can also be run on Windows environment without installing any specific software or framework as such.
Note: Please be sure to clear your temp folder as it adds lot of temporary files in every run.
In local mode, this application can be run from command line by invoking:
mvn clean compile exec:java -Dexec.classpathScope=compile -Dexec.mainClass=org.p7h.storm.sentimentanalysis.topology.SentimentAnalysisTopology
or
mvn clean compile package && java -jar target/storm-sentiment-analysis-0.1-jar-with-dependencies.jar
Distributed mode requires a complete and proper Storm Cluster setup. Please check Apache Storm wiki for setting up a Storm Cluster.
In distributed mode, after starting Nimbus and Supervisors on individual machines, this application can be executed on the master [or Nimbus] machine by invoking the following on the command line:
storm jar target/storm-sentiment-analysis-0.1.jar org.p7h.storm.sentimentanalysis.topology.SentimentAnalysisTopology SentimentAnalysis
Note: Repo's recent update to Storm v0.9.3 was not tested in Cluster mode. But it should work as before, if the cluster setup is all fine.
If you find any issues, please report them either raising an issue here on GitHub or alert me on my Twitter handle @P7h. Or even better, please send a pull request.
Appreciate your help. Thanks!
Copyright © 2013-215 Prashanth Babu.
Licensed under the Apache License, Version 2.0.
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