/social-media-analysis

Social Media Analysis, scalable solution, flexible deployment that analyses social media contents

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Social Media Analysis

End-to-end solution for analysing social media contents
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Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. Roadmap
  5. Contributing
  6. License
  7. Acknowledgments

About The Project

Social Media Analysis

This is a simple and scalable end-to-end solution that analyses tweets & users of selected topics. Here, tts made to be crypto / blockchain related, such as crypto KOLs, applications and coins. It deploys

  • Scalable crawler that crawls
    • Tweets of topics mentioning influencers such as Elon Musk, Vitalik, applications such as DeFi, Metaverse, coins suchs as $BTC, $ETH, $ADA
    • Users mentioning the tweets above, are snapshotted daily
  • Stream ingestion of crawled data into data warehouse
    • Tweets data
    • Users data
  • Clean and transform data into dimensions that models
    • Coin interests - grouped by crypto KOLs
    • Coin interests - grouped by crypto applications
  • BI Dashboard - shows insights of coin interests in different segment of social media users

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Built With

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Getting Started

Currently, the setup can only be done in a single machine. Everything is in containers, runs on docker-compose, so you will need docker installed.

Prerequisites

You will need docker engine & docker-compose cli such as docker-compose. I am on MacOS and use a combination of colima + docker-compose

  • docker-compose cli

    $ brew install docker-compose

You will need a Google Cloud Platform account in order to setup BigQuery and the credentials in order for it to work

You will need python libraries and dependency manager such as pip. I use poetry

  • Poetry

    $ curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python -

Installation

  1. Clone the repo

    $ git clone https://github.com/koksang/social-media-analysis.git

  2. To install dependencies locally especially DBT, you can do

    • Poetry

      $ poetry install

    Or if you use pip

    • Pip 
      $ poetry export -f requirements.txt --without-hashes
$ pip install -r requirements.txt

---> 100%

  3. Start docker-compose.yaml by doing as below. It will start in detached mode

    $ docker-compose -f infra/docker-compose.yaml up -d

  4. Set environment variables

    • Google Cloud specific

      $ export GOOGLE_APPLICATION_CREDENTIALS=${YOUR PATH TO GOOGLE_APPLICATION_CREDENTIALS}
$ export GOOGLE_PROJECT_ID=${YOUR GOOGLE_PROJECT_ID}

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Usage

Todo

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Roadmap

  • Deploy BI Dashboard for insights visualization
  • Finalize docker-compose deployment
  • Convert docker-compose deployment into K8s
  • Use other BI dashboards (such as: apache/superset)
  • Crawl more
    • Nested tweet replies
    • Top trends
    • User profiles snapshot

See the open issues for a full list of proposed features (and known issues).

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Acknowledgments

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