Text Classification Algorithms: A Survey (1904.08067v5)

Abstract: In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in the real-world problem are discussed.

A Survey of Text Classification Algorithms

Text classification, a fundamental task in NLP, has seen significant advancements due to modern machine learning techniques. Kowsari et al.'s paper provides an in-depth survey of various algorithms used for text classification, highlighting the intricate processes involved in converting raw text data into meaningful categorizations.

Overview of the Text Classification Process

The text classification pipeline generally includes four key phases:

1. Feature Extraction: This involves converting raw text data into structured formats that algorithms can process. Techniques used include Term Frequency-Inverse Document Frequency (TF-IDF), term frequency (TF), word embeddings such as Word2Vec, Global Vectors for Word Representation (GloVe), and FastText, along with contextualized word representations for capturing semantic meanings.
2. Dimensionality Reduction: To handle the high dimensionality of text data, methods like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and non-negative matrix factorization (NMF) are employed. Advanced methods such as random projection, autoencoders, and t-distributed stochastic neighbor embedding (t-SNE) further reduce computational complexity.
3. Classification Techniques: Various algorithms, from traditional methods like logistic regression (LR), Naive Bayes classifiers (NBC), and k-nearest neighbor (KNN) to advanced methods like support vector machines (SVMs), decision trees, random forests, conditional random fields (CRF), and deep learning models (CNNs, RNNs, LSTMs, and deep belief networks), are utilized for categorizing text. Each method has its strengths and limitations in handling text data's complexity and dimensionality.
4. Evaluation: The effectiveness of classification models is measured using metrics such as accuracy, F-score, Matthews correlation coefficient (MCC), receiver operating characteristics (ROC), and the area under the ROC curve (AUC). These metrics help in comparing different models' performance objectively.

Detailed Discussion of Techniques

Feature Extraction

• Weighted Words and TF-IDF: These techniques are simple to compute and effective for many applications but fall short in capturing semantic relationships and order of words.
• Word Embeddings: Methods like Word2Vec and GloVe capture semantic meanings but fail to handle polysemy effectively. FastText addresses out-of-vocabulary words but is computationally intensive.
• Contextualized Word Representations: These capture semantic meanings in context, training on specific contexts within text, but are computationally demanding and require significant memory.

Dimensionality Reduction

• PCA and LDA: Useful but computationally expensive.
• Random Projection: Faster but less effective on small datasets.
• Autoencoders and t-SNE: Provide detailed data reduction and visualization capabilities but introduce complexity in training and interpreting results.

Classification Techniques

• Traditional Methods (Rocchio, Bagging, Boosting, LR, NBC, KNN, SVM): Well-understood and widely used with clear advantages and limitations. SVMs, notably, are robust against overfitting due to high-dimensional text data.
• Tree-Based Methods (Decision Trees, Random Forests): Fast training and interpretability, but sensitive to data perturbations and prone to overfitting in some cases.
• Graphical Models (CRF): Effective for sequence labeling and handling contextual dependencies in text.

Deep Learning

• CNN, RNN, LSTM, GRU, HAN: Offer robust models for capturing text structure and semantics. However, they are data-intensive, computationally expensive, and offer less interpretability.
• Ensemble Learning (RMDL, HDLTex): Combines multiple models to improve overall performance, leveraging different optimizers and structures, but at the cost of increased computational resources.

Implications and Future Directions

The practical implications of these techniques span various domains, including:

• Information Retrieval: Enhances search engines and document categorization.
• Sentiment Analysis: Enables understanding of public opinion from social media data.
• Recommender Systems: Improves user experience by predicting preferences based on text reviews.
• Healthcare: Assists in medical record analysis and patient data categorization.
• Legal: Facilitates the classification and retrieval of legal documents.

Conclusion

Kowsari et al.'s survey underscores the diverse techniques and models available for text classification, each with unique properties and suitable applications. The paper provides a comprehensive understanding that can guide researchers and practitioners in selecting and optimizing the appropriate algorithms for their specific needs, ensuring advancements in the evolving field of NLP and text classification. Future developments are likely to focus on enhancing model interpretability, improving computational efficiency, and expanding the robustness of techniques to handle complex, high-dimensional text data more effectively.