Optimizing Multi-Class Text Classification Models for Imbalanced News Data

In the age of abundant digital journalism, the categorization of news articles has become increasingly crucial for efficient information retrieval and analysis. This article examines different machine learning and deep learning methods used in classifying news articles, with a specific focus on overcoming challenges presented by imbalanced datasets. Conventional techniques like Logistic Regression, Random Forest, and Decision Trees provide interpretability but face difficulties in achieving precision, especially in imbalanced scenarios. Advanced models such as Multi-Class CNN-LSTM, MLP, and RNN exhibit superior performance by effectively capturing both local and global features in news articles. Assessments across various categories showcase the flexibility and reliability of these models, with MLP consistently surpassing others. The research underscores the significance of choosing tailored models and highlights notable progress in news categorization accuracy through advanced methodologies. Ongoing exploration in deep learning and ensemble strategies shows potential for further improvements in tackling the evolving challenges of news categorization within the expansive realm of digital journalism and big data.