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BAE -- Brand Analytics Environment

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For users or potential users:

For developers:

Structure of BAE:

arch

Prerequisite:

  • You have Nodejs installed: https://nodejs.org/en/download
    • In the root directory of BAE, install required libraries npm install
  • You have Python3 installed: https://www.python.org/download/releases/3.0/
    • Use pip3 or Aniconda to install required python libraries pip3 install boto3 scikit-learn numpy
  • Place a config.json file under the path root directory and another config.json file under path scripts/
    • These two files contain credentials to acess twitter, AWS, and mashape.
    • content of config.json looks like this:

    { "twitter":{ "consumerKey":"", "consumerSecret":"" }, "aws":{ "accessKey":"", "accessKeySecret":"" }, "mashape":{ "apiKey":"************" } }

    • You have to set up AWS access to AWS lambda, and S3 bucket.

Configuration:

  • Clone this repository to your local disk. git clone https://cwang138@opensource.ncsa.illinois.edu/bitbucket/scm/smm/smm-bae.git BAE
  • Test the BAE server npm test
  • Run the BAE server npm start. BAE runs on port 8001: http://localhost:8001

Computation:

  • The business logic is handled by this BAE server and meanwhile the computation part is deployed on AWS Lambda. You can find the deployed python code in smm-analytics repository
    • bae_botometer/ contains script that checks if a twitter screen name is a bot or not
    • bae_check_screen_name/ contains scripts that check if the input twitter screen names exist or not
    • bae_collect_timeline/ crawl the most recent twitter status of a given screen name through Twitter's API
    • bae_get_personality/ retreive IBM personality scores via IBM Cloud's API
    • bae_get_sim_score/ calculates the cosine similarity score of two screen names' personality score
    • bae_bulk_comparison/ compare multiple persons personality scores and calculates the consine similarity matrix

Please contact the SRTI lab to request assistance on setting up the BAE server.

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