Iserali-Palestine conflict classifer

Table of Contents

1. About The Project
2. Acknowledgments

About The Project

This project is a Iserali-Palestine conflict classifer. Using Naive Bayes as baseline model, and bidirectional transformer as the main language model, the project involves inputting news articles into the system, which then annotates them to categorize content related to either Israel or Palestine. This classification aims to segregate information and provide clear insight into the biases present in the news. The main challenge in this study is the limited dataset available, which can affect the model's accuracy in detecting subtle biases and its overall robustness. Despite these challenges, careful model training and data management are key to understanding and categorizing informational bias effectively.

Project is built with: python 3.8.8, colab

(back to top)

Acknowledgments

• In Plain Sight: Media Bias Through the Lens of Factual Reporting
• SemEval-2016 Task 6: Detecting Stance in Tweets
• knowledge on bert and naive bayes

(back to top)