1. Introduction

2. Text Preprocessing  
2.1. Text Cleaning and Preprocessing  
2.2. Syntactic Word Representation  
2.3. Weighted Words  
2.4. Word Embedding  
2.5. Limitations

3. Dimensionality Reduction  
3.1. Component Analysis  
3.2. Linear Discriminant Analysis (LDA)  
3.4. Random Projection

4. Existing Classification Techniques  
4.1. Rocchio Classification  
4.2. Boosting and Bagging  
4.3. Logistic Regression  
4.4. Naïve Bayes Classifier  
4.5. K-Nearest Neighbor  
4.6. Support Vector Machine (SVM)  
4.7. Decision Tree  
4.8. Random Forest  
4.10. Deep Learning

5. Evaluation  
5.1. Macro-Averaging and Micro-Averaging  
5.2. Fβ Score  
5.3. Matthews Correlation Coefficient (MCC)  
5.4. Receiver Operating Characteristics (ROC)  
5.5. Area Under ROC Curve (AUC)

6. Discussion  
6.1. Text and Document Feature Extraction  
6.2. Dimensionality Reduction  
6.3. Existing Classification Techniques  
6.4. Evaluation

7. Text Classification Usage  
7.1. Text Classification Applications  
7.2. Text Classification Support

8. Conclusions