Abstract:
"Searching, retrieving, and arranging text in ever-larger document collections necessitate
more efficient information processing algorithms. Document categorization is a crucial component of
various information processing systems for supervised learning. As the quantity of documents grows, the
performance of classic supervised classifiers has deteriorated because of the number of document categories.
Assigning documents to a predetermined set of classes is called text classification. It is utilized extensively
in a wide range of data-intensive applications. However, the fact that real-world implementations of these
models are plagued with shortcomings begs for more investigation. Imbalanced datasets hinder the most
prevalent high-performance algorithms. In this paper, we propose an approach name multi-class Convolutional Neural Network (MCNN)-Long Short-Time Memory (LSTM), which combines two deep learning
techniques, Convolutional Neural Network (CNN) and Long Short-Time Memory, for text classification in
news data. CNN’s are used as feature extractors for the LSTMs on text input data and have the spatial structure
of words in a sentence, paragraph, or document. The dataset is also imbalanced, and we use the Tomek-Link
algorithm to balance the dataset and then apply our model, which shows better performance in terms of F1-
score (98%) and Accuracy (99.71%) than the existing works. The combination of deep learning techniques
used in our approach is ideal for the classification of imbalanced datasets with underrepresented categories.
Hence, our method outperformed other machine learning algorithms in text classification by a large margin.
We also compare our results with traditional machine learning algorithms in terms of imbalanced and
balanced datasets."
