Automated News Article Classification Using Text Mining

Introduction

Automated news article classification is a crucial task in the field of natural language processing (NLP) and information retrieval. The objective is to categorize news articles into predefined categories such as politics, sports, business, and technology. This project proposal outlines a system that leverages text mining techniques to improve the accuracy and efficiency of news article classification.

Background

Recent research has demonstrated that machine learning models can significantly enhance the performance of news article classification systems. These systems utilize various text features such as term frequency-inverse document frequency (TF-IDF), n-grams, and word embeddings to classify articles accurately. The use of deep learning models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) has been particularly effective in capturing the semantic meaning of text.

Project Objective

The primary objective of this project is to develop a robust news article classification system using a combination of traditional machine learning models and deep learning techniques. This system aims to improve upon existing methods by incorporating advanced feature extraction techniques and leveraging large-scale labeled datasets.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets such as the AG News Classification Dataset for training and evaluation.
• Text Preprocessing: Perform operations such as removing HTML tags, stop words, and punctuation, as well as applying stemming and lemmatization.

2. Feature Extraction

• TF-IDF: Use TF-IDF to transform text into numerical representations.
• N-Grams: Extract uni-grams, bi-grams, and tri-grams to capture contextual information.

3. Model Architecture

• Machine Learning Models: Implement models such as logistic regression, decision trees, and support vector machines (SVM).
• Deep Learning Models: Explore CNNs and RNNs for capturing complex patterns in the text data.

4. Training and Evaluation

• Training: Use cross-validation techniques to train the models effectively.
• Evaluation Metrics: Measure performance using metrics such as accuracy, precision, recall, and F1-score.

Expected Outcomes

The proposed system is expected to achieve higher accuracy in news article classification compared to traditional methods. By utilizing a combination of machine learning and deep learning techniques, the system should effectively handle variations in language usage across different articles.

Conclusion

This project aims to advance the field of automated news article classification by developing a state-of-the-art system capable of accurately categorizing news articles. The integration of traditional machine learning models with deep learning architectures is anticipated to provide significant improvements in performance.

For further details on related research, please refer to the paper "Automated News Article Classification Using Text Mining," available at https://ieeexplore.ieee.org/document/8768808.

The dataset used for this project can be accessed at https://www.kaggle.com/amananandrai/ag-news-classification-dataset/notebooks.