🇮🇳 🤖 Social Media Intelligence for Brand Analysis 🚀🔥

Goals of this project:

• Build a real time social media analytics tool
• Understand how social media users from different regions react to a specific brand
• Use Sentiment Analysis and Named Entity Recognition to understand the user's perceptions towards different entities.

Live implementation:

Click here to see Twitter account posting tweets

Tech Stack of this Project:

• Language: Python3
• Dependencies: APS Scheduler, PyTesseract, GSpread
• Libraries: Available in requirements.txt.

To run this Project:

• Clone this repo and pip install the requirements

git clone https://github.com/VirtualGoat/Twitter-Automation.git
cd Twitter-Automation
pip install -r requirements.txt
python automation_script.py

Flow of project:

1. User uploads an image containing text

2. The text from the image gets extracted using Pytesseract

3. The extracted text gets stored in an online google sheet

4. The stored text gets popped row by row from the Google sheet and uploaded on Twitter at 5 hour intervals

This project still has scope of development, so you can also contribute to this Project as follows:

• Fork this Repository.
• Clone your Fork on a different branch:
  • git clone -b <name-of-branch> https://github.com/VirtualGoat/Twitter-Automation.git
• After adding any feature:
  • Go to your fork and create a pull request.
  • I will test your modifications and merge changes.

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Useful links referred by me:

https://dev.to/emcain/how-to-set-up-a-twitter-bot-with-python-and-heroku-1n39

https://medium.com/datadriveninvestor/making-a-quote-tweeting-twitter-bot-with-python-tweepy-and-heroku-69a11cd3f47e

https://medium.com/better-programming/introduction-to-apscheduler-86337f3bb4a6

The last link has finally been followed by me. It uses APScheduler.

Developed with ❤️ by:

Parth Nagarkar