Text Classification for News Aggregation

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Project Overview:

Objective

The “Text Classification for News Aggregation” project aims to create a dataset for training machine learning models to accurately classify news articles into various categories or topics. This dataset, tailored for the Text Classification for News Aggregation initiative, will support news aggregators, content recommendation systems, and information retrieval applications.

Scope

This project involves collecting news articles from various sources, such as news websites, blogs, and RSS feeds, and annotating them with relevant category or topic labels to facilitate efficient news aggregation and content organization.

Sources

News Websites: Gather news articles from reputable news websites covering a wide range of topics, including politics, sports, technology, and entertainment.
Blogs and Opinion Pieces: Collect articles from blogs and opinion websites that offer diverse perspectives on current events and topics.
RSS Feeds: Access RSS feeds from news sources and blogs to continuously collect updated content.

Data Collection Metrics

Total News Articles for Classification: 50,000 articles
News Websites: 30,000
Blogs and Opinion Pieces: 10,000
RSS Feeds: 10,000

Annotation Process

Stages

Text Classification: Annotate each news article with category or topic labels, indicating the primary subject matter, such as “Politics,” “Sports,” “Technology,” “Entertainment,” etc.
Metadata Logging: Log metadata, including the article title, publication date, source URL, and any additional contextual information.

Annotation Metrics

News Articles with Classification Labels: 50,000
Metadata Logging: 50,000

Quality Assurance

Stages

Annotation Verification: Implement a validation process involving subject matter experts or journalists to review and verify the accuracy of category or topic labels.
Data Quality Control: Ensure the removal of articles with poor quality content, spam, or irrelevant information.
Data Security: Protect sensitive information and adhere to copyright and licensing regulations.

QA Metrics

Annotation Validation Cases: 5,000 (10% of total)
Data Cleansing: Remove low-quality or irrelevant articles

Conclusion

The “Text Classification for News Aggregation” dataset is a valuable resource for news aggregators, content recommendation systems, and information retrieval applications. With accurately annotated news articles and comprehensive metadata, this dataset empowers the development of advanced text classification models that can automatically categorize and organize news content for users. It contributes to improved news aggregation, personalized content recommendations, and efficient access to information across a wide range of topics and sources.