This project focuses on analyzing and predicting article engagement and popularity using named entity recognition, feature engineering, and machine learning models. The project includes feature extraction, model training, and visualization to gain insights into the relationship between named entities and article performance metrics.
- Sentiment analysis and title-related feature engineering.
- Named entity-based text vectorization.
- Random Forest classification with SMOTE for handling class imbalance.
- Evaluation metrics like accuracy, confusion matrix, ROC-AUC score.
- Visualization of entity relationships and engagement.
- Clone this repository:
git clone https://github.com/Gh-Novel/NER.git
cd NER- Install dependencies: Make sure you have Python 3.8 or higher installed. Then run:
pip install -r requirements.txt- The process to execute the code is linear after insure the dataset folder and the NER notebook in same place just execute the cell line one by one
Google colab: https://colab.research.google.com/drive/1177FLtC_SXdHpAp3wqCdmBh63v5pWsFJ