Welcome to SMUG’s documentation!

Contents:

• Products
• Queues
  • Queue types
  • Real world usage
• Data Schema
  • smug_reports
  • smug_messages
• smug package
  • Subpackages
  • Submodules
  • smug.callback_helper module
  • smug.connection_manager module
  • smug.mongo_manager module
  • smug.run module
  • smug.send_to_smug_helper module
  • Module contents

# Social Media User Grouper (SMUG)

SMUG is a profiler which allows you to group social media users using big data. SMUG provides extra insight into tweets by running various kinds of analytics on it.

For example it can give a propibility about if a giving tweet is about someone being sick. This is accomplished by analysing the words in the tweet using deep learning. # Dependencies In order to use the project the following dependencies have to be satisfied * Docker * Docker-Compose * Python3

# Installation

1. run pip install -r requirements.txt
2. Afterwards run python -m textblob.download_corpora to enable the NLP functionality. These include some third-party licenses.

# Usage instructions This project uses docker-compose thus to run the project you need to issue the command docker-compose up. This will pull all the required images and runs all the containers. There are several things needed to run the project correctly:

• .env file. See resources/.env.example for the needed parameters.
• Twitter data. This can be in the form of a csv or streaming data if configured correctly.

Optional: * A trained word2vec model for use with WordVectoring analysis.

• If you don’t have a trained model utils/word_vectoring_model_generator.py can do this for you. You will need to place a wikipedia dump in the resources folder and alter the script to use the correct name.

• A Gaussian Mixture Model for location prediction modules.

  • A trained model can be created using the included utility.

# Technical information ## Local development While developing you can opt to run the python files locally on your system. This can be done by running the individual python files. A run.py is also which allows you to start multiple workers at once. run.py start all the workers necessary for basic processing. Now all you need is data. This can be done by running importers/coosto_importer.py which will prompt you for a csv file. This csv file is then put into SMUG. This csv file should be located somewhere in the resources folder ## Command ordering In order to ensure the docker environment contains the correct settings and queues it is important that the initializer.py file is ran every time the docker environment is restarted. After running the initializer the order of execution is not important. When using run.py this is done automatically for you.

Indices and tables

• Index
• Module Index
• Search Page