https://github.com/priyanshu501/news_article_classification_using_nlp_and_deep_learning

This project leverages Natural Language Processing (NLP) techniques and deep learning to classify news articles into different categories.
https://github.com/priyanshu501/news_article_classification_using_nlp_and_deep_learning

classification data-science deep-learning keras lstm machine-learning nlp python

Last synced: 29 days ago
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This project leverages Natural Language Processing (NLP) techniques and deep learning to classify news articles into different categories.

• Host: GitHub
• URL: https://github.com/priyanshu501/news_article_classification_using_nlp_and_deep_learning
• Owner: Priyanshu501
• License: mit
• Created: 2024-07-22T14:00:13.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-22T16:07:06.000Z (4 months ago)
• Last Synced: 2024-10-11T04:41:16.781Z (29 days ago)
• Topics: classification, data-science, deep-learning, keras, lstm, machine-learning, nlp, python
• Language: Jupyter Notebook
• Homepage:
• Size: 2.27 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: news_classification.ipynb
  • License: LICENSE

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README

        
## Introduction

This project leverages Natural Language Processing (NLP) techniques and deep learning to classify news articles into different categories. The dataset used in the collection of news articles from BBC.

## Objective

The primary objective of this project is to develop a robust text classification model capable of accurately categorizing news articles. The project aims to demonstrate the practical appliation of NLP and deep learning in solving real-world text classification problems.

## Implementation

1. Data Collection and Preprocessing:

    * **Dataset**: The BBC dataset contains approximately 2000+ news articles across 5 categories.

    * **Pre-processing Steps**: 

        * Tokenization using Keras Tokenizer.

        * Padding sequences to a uniform length using Keras pad_sequences.

        * Encoding lables using scikit-learn's LabelEncoder.

2. Model Development:

    * Architecture:

        * **Embedding Layer**: Converts words into dense vectors of fixed size.

        * **LSTM Layer**: Long Short-Term Memory network to capture dependencies in text.

        * **Dense Layer**: Fully connected layer with softmax activation for classification.

    * Hyperparameters:

        * Vocabulary Size: 20,000

        * Sequence Length: 1,000

        * Embedding Dimension: 128

        * LSTM Units: 128

## Summary

This project showcases the practical application of NLP and deep learning in text classification, by developing a scalable, interpretable, and user-friendly solution.