A Hadoop Wordcounter Job - Retrieves tweets and runs a MapReduce wordcounter for sentimental analysis
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Hadoop Tweet Wordcounter Job

What is it?

A tweet retriever & hadoop wordcounter job. Built to show the hadoop hdfs usage for a 'CSC338: Parallel & Distributed Processing' group project at Missouri State University.

Why use it?

For sentimental analysis of the most recent tweets containing a particular hashtag. For example, if you run the job on tweets containing the hashtag #food, you may be able to make conclusions about the most discussed meals within a given amount of time the tweets were retrieved.

How it works

There are 2 major steps in running the project:

  1. Retrieve tweets via twitter-api by hashtag and put them into a textfile
  2. Run a wordcounter script with Hadoop to count the number of word occurences in the retrieved tweets file

Steps to setup & run:

  1. Make sure you have hadoop 2.7.3 and python 3 installed on your server or local computer where you will be hosting the project.

  2. Run the tweet retriever script with ./get-tweets.sh #hashtag-word, where #hashtag-word is your desired hashtag. To quit the script after desired amount of tweets are retrieved, use 'Ctrl-C'.

  3. To copy the textfile to HDFS & run the hadoop job on the file, run ./job.sh [textfilename.txt]. NOTE: The default textfile created from the tweet retriever in step 2 is 'fetched_tweets.txt', so this should be used unless you plan to use a different textfile in the hadoop wordcounter.

Viewing the Output:

  1. Assuming the job completes successfully, the output will be placed in the local folder where the repository files are located.
  2. Open and view the textfile inside the '/completed-wordcount' directory. Words are sorted by most common occurences to least common occurences


  • There is a serial wordcount script that can be used in lieu of the Hadoop job script. Run python serial-wordcount.py [filename.txt] instead of the job.sh script.
  • Commands to manipulate the HDFS begin with hdfs dfs, followed by the command to execute with any other arguments
  • hdfs dfs -ls [directory name] can be used to verify files were copied to the HDFS
  • If you need to create a directory on the HDFS, run hdfs dfs -mkdir /directory-name
  • You can type 'hadoop-streaming-*.jar' instead of remembering the exact version number when accessing the hadoop jar file
  • $PWD gives the current working directory

External Resources & Documentation:

  1. The Mapper & Reducer are based on this Hadoop Application Walkthrough