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IEEE NITK project on a Twitter Feed Filtering System for improved relevance and reduction of irrelevant tweets

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Twitter-Feed-Filter

Status

As of late 2023, all methods to obtain Twitter data require authentication. This was not the case when the project was built. Hence, this feature is not supported now. But all the methods remain exactly same. The authentication feature can be added in the future (Open a PR for the same if interested).

Motivation for the Project

This is a project done under IEEE NITK where the goal is to identify the irrelevant tweets with respect to a particular trend and removing them from the User's feed. It helps in keeping the feed clean and improves the User experience while using the application.

An external twitter scraper is being which scrapes the tweets using Golang. The steps to install it are as follows:

Requirements before downloading the Twitter Scraper

Install Golang from the website

Enable dependency tracking for your code

For enabling dependency tracking of the code you have to create a go.mod file. [path-to-module] needs to be the location of a remote repository from where Go can download the source code.
Following are the steps to create [path-to-module].

<prefix>/<descriptive-text>

Example: github.com/

  • Here it is <github.com/your-username/twitter-scraper>.
  • Now, run the command
go mod init [path-to-module]

Installation of the scraper

go get -u github.com/n0madic/twitter-scraper

Setting up the Project

Clone the repository using the HTTPS key:

git clone https://github.com/helios2003/Twitter-Feed-Filter.git

Or you can fork the repository and git clone it from there.

Navigate to the root directory and do the following:

For Windows

python -m venv venv
venv/Scripts/activate
pip install -r src/python/requirements.txt

For Linux

python -m venv venv
source venv/bin/activate
pip install -r src/python/requirements.txt

Navigate to flask directory for how to run the web app.

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IEEE NITK project on a Twitter Feed Filtering System for improved relevance and reduction of irrelevant tweets

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