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💡[Feature]: Category Classifiaction of News using LSTM #1579

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Stuti333 opened this issue Oct 26, 2024 · 3 comments
Closed
4 tasks done

💡[Feature]: Category Classifiaction of News using LSTM #1579

Stuti333 opened this issue Oct 26, 2024 · 3 comments
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enhancement New feature or request

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@Stuti333
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Is there an existing issue for this?

  • I have searched the existing issues

Feature Description

LSTM-based news category classification is a deep learning approach that automatically categorizes news articles into predefined categories (like politics, sports, technology, etc.). The LSTM (Long Short-Term Memory) network processes sequences of words from news text, capturing long-term dependencies and contextual information that are crucial for understanding the article's content. The model learns patterns in word sequences during training to predict the most appropriate category for any given news article input.

Use Case

Automatically sorting incoming news articles on platforms like Google News
Personalizing news feeds based on user category preferences
Organizing large archives of news articles in media organizations

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Medium

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  • I have read the Contributing Guidelines
  • I'm a GSSOC'24 contributor
  • I want to work on this issue
@Stuti333 Stuti333 added the enhancement New feature or request label Oct 26, 2024
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Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. If you have any questions reach out to LinkedIn. Your contributions are highly appreciated! 😊

Note: I Maintain the repo issue twice a day, or ideally 1 day, If your issue goes stale for more than one day you can tag and comment on this same issue.

You can also check our CONTRIBUTING.md for guidelines on contributing to this project.
We are here to help you on this journey of opensource, any help feel free to tag me or book an appointment.

@sanjay-kv
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please read the pinned issue

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Hello @Stuti333! Your issue #1579 has been closed. Thank you for your contribution!

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