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When first trying to run the program, pycharm will mention all of the packs that needs to be installed, this can be done with pycharm: Orca should be installed via Anaconda. If orca is installed in the main folder of Anaconda3, copy orca.cmd to envs/learning orca: https://github.com/plotly/orca If the computer has 8gb or less of RAM, JVM needs to be allocated more memory than default. Navigate to sparkfolder/conf edit file spark-default.conf.template by making a copy of the fil and removing .template from the file extension. Uncomment the line spark.driver.memory and set it to "spark.driver.memory 5g". Running the program: First, run the file called stemming.py Then run Bayes.py. For the best result run it in an IDE with a console. We recommend Pycharm as that is what we developed it in and if any dependencies are missing, Pycharm will notify you. When done correctly the output of the program will be a .png file that visualizes the sentiment for each month, and a .csv file in the prediction folder that shows the prediction for the whole dataset. potential errors: -Make sure that in the environment variables Java_Home is pointing to jdk8 - If "ValueError: For some reason, plotly.py was unable to communicate with the local orca server process, even though the server process seems to be running": This can be a timeout error, which we have experienced with one of our slow machines. This only affects the visualization, but the machine-learning has been completed, so we have attached a copy of the visualization in case. - The folder "predictions" will have to be deleted if the program is to be run after it's cration
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