Text Classification Algorithms - A Survey

Feature Extraction

TF-IDF

TF

Word2Vec

Global Vectors for Word Representation (GloVe)

Dimension reduction

Principal Component Analysis (PCA)

Linear Discriminant Analysis (LDA)

non-negative matrix Factorization (NMF)

Random Projection

Autoencoder

t-distributed stochastic neighbor enbedding (t-SNE)

Classifier Selection

Rocchio classification

ensemble-based learning

Boosting

Bagging

Logistic Regression (LR)

The Naive Bayes Classification (NBC)

Non-parameter techniques

k-nearest neighbor (KNN)

Support Vector Machine (SVM)

Tree-based classifiers

Decision tree

Random Forest

Graphical classifications

Conditional Random Fields (CRFs)

Deep learning

Evaluation

Fbeta Score

Matthews Correlation Coefficient (MCC)

Receiver Operating Characteristics (ROC) Area Under the ROC curve (AUC)

References

• Text Classification Algorithms: A Survey. Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa, Sanjana Mendu, Laura E. Barnes, Donald E. Brown