A Complete Process of Text Classification System Using State-of-the-Art NLP Models

Area of Science:

• Computer Science
• Artificial Intelligence
• Natural Language Processing

Background:

• Exponential growth of online text data necessitates advanced text mining.
• Machine and deep learning models are crucial for analyzing diverse text documents.
• Text classification is a fundamental task in natural language processing (NLP).

Purpose of the Study:

• To summarize state-of-the-art machine and deep learning models for text classification.
• To discuss the advantages and disadvantages of various text classification algorithms.
• To provide a comprehensive overview of literature and subtasks in text classification.

Main Methods:

• Review of supervised, semi-supervised, and unsupervised learning models.
• Analysis of machine learning and deep learning algorithms for text classification.
• Synthesis of existing literature on text classification techniques.

Main Results:

• Identification of key machine and deep learning algorithms for text classification.
• Evaluation of the strengths and weaknesses of different text classification approaches.
• Compilation of relevant subtasks and literature for text classification.

Conclusions:

• The paper provides a valuable resource for understanding text classification methods.
• It highlights areas for potential improvement and innovation in text mining.
• Readers can leverage this review to develop new text classification techniques for various domains.