The workflow contains three workers (seeds):
tweetinject.py- reads a file with tweets and sends them to the queuefilter.py- performs sentiment analysis on incoming tweetscollector.py- counts tweets and writes result into a file
pip install pika
You should copy required files to you VM (as it is explained in the assignment).
You should copy desired workers to ~/pmk-seeds directory - or directory that you specify with --taskdir option (see below in section Run Pumpkin).
Do the following if you want to run all three workers on one VM:
scp *.py pumpkin:pmk-seeds/
Copy trained classifier to required directory.
mkdir -p /home/pumpkin/nltk_data/classifiers
scp movie_reviews_NaiveBayes.pickle pumpkin1:nltk_data/classifiers/
Complete (see slides) and copy pumpkin configuration file into working directory
scp pumpkin.cfg pumpkin1:pumpkin/
You should also change a group name. With the same value for different groups, your workers might communicate with workers of other people since the messages are identified in the system by a pair: message type and a group.
In order to start pumpkin, execute: Replace $taskdir by a chosen directory such as '~/pmk-seeds'
python DRHarness.py --supernode --taskdir $taskdir --broadcast --endpoints="tcp://*:*" --gonzales
Perform the computations as it is explained in the assignment. Then, you plot a graph that shows how the number of positive and negative tweets changes in time. First, if needed, sort the data:
sort --output=tweetstats.data tweetstats.data
and plot a graph (using gnuplot):
gnuplot plot.gnu
open the file tweetstats.png.