📰 Named Entity Recognition (NER) and Predictive Modeling for News Articles 📊

🎯 Objective

The goal of this task is to analyze a set of news articles through Named Entity Recognition (NER) and to engineer numerical features based on these entities for predictive modeling. The task involves:

• Extracting named entities from articles using an open-source LLM model.
• Creating insightful features.
• Building a predictive model to determine article popularity based on engagement metrics.

This task assesses skills in natural language processing (NLP), feature engineering, and predictive analytics. 🧠

📂 Data Collection

• The provided dataset of news articles can be accessed here: News Articles Dataset

🧹 Text Preprocessing

1. Cleaning the Text:
   • Remove unnecessary whitespace, HTML tags, and special characters.
   • Normalize text (convert to lowercase).
2. Tokenization & Stop Words Removal:
   • Tokenize the text and remove stop words using libraries like NLTK or SpaCy.

🤖 Named Entity Recognition (NER)

1. Entity Extraction:
   • Use an open-source LLM model SpaCy to extract named entities from the cleaned articles.
2. Entity Categorization:
   • Ensure entities are categorized correctly into types such as Organizations (ORG), Locations (GPE), and People (PERSON).
3. Entity Frequency:
   • Create numerical features based on the frequency of each entity type in each article (e.g., count of organizations, locations, and people).

🔧 Feature Engineering

1. Additional Features:
   • Article length (number of words).
   • Sentiment scores (using TextBlob or VADER).
   • Engagement metrics (likes, shares, comments) if available in the dataset.
2. Innovation:
   • Derive creative features beyond the basic entity counts to enhance the feature set.

📈 Predictive Modeling

1. Model Selection:
   • Train a predictive model using the engineered features to predict article popularity. Possible models include Linear Regression, Random Forest, etc.
2. Model Evaluation:
   • Evaluate the model performance using accuracy scores, F1-scores, or mean absolute error (MAE).

📊 Visualization

1. Visualizations:
   • Create visualizations to show the relationship between named entities and article popularity.
   • Use libraries like Matplotlib or Seaborn to generate:
     • Bar charts for entity frequency.
     • Scatter plots illustrating correlations.
     • Heatmaps for the relationship between entity counts and engagement metrics.

Example Visualizations:

🏆 Popularity vs. Named Entity Count

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📊 Heatmap

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📝 Findings & Insights

• Named Entities Impact: The frequency of organizations and locations in news articles tends to correlate positively with engagement metrics such as likes and shares.
• Sentiment Analysis: Articles with more positive sentiment tend to receive higher engagement compared to those with neutral or negative sentiment.
• Article Length: Articles with a higher word count tend to have more named entities, which in turn leads to better engagement.

🚀 Next Steps

• Experiment with more sophisticated NLP techniques for entity extraction and feature engineering.
• Explore different predictive models and evaluation metrics to improve accuracy and generalizability.

📅 License

This project is licensed under the MIT License - see the LICENSE file for details.

📢 Acknowledgements

• Dataset: News Articles Dataset
• Libraries: SpaCy, TextBlob, NLTK, Matplotlib, Seaborn

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Feel free to open an issue or submit a pull request if you have any suggestions or improvements! 💬