Text mining

1. Text mining Gergely Kótyuk Laboratory of Cryptography and System Security (CrySyS) Budapest University of Technology and Economics www.crysys.hu

2. Introduction • Generic model • Document preprocessing • Text mining methods

3. Text Mining Tasks • Classification (supervised learning) • Binary classification • Single label (multi-class) classification • Multi-label classification • Multi-level (hierarchical) classification • Clustering (unsupervised learning) • Summarization • Extraction: only parts of the original text • Abstraction: introduces text that is not included in the original text

4. Solutions • Classification • Decision tree • Neural network • Bayes network • Clustering • k-means

5. Document preprocessing • Goal: represent any text briefly, in a fixed number of parameters • Representation: vector space model

6. Vector space model • The text is tokenized to words • The words are canonized to base wordswe refer to base words as terms • A dictionary is built, that is the set of the terms in the document • The document is represented as a vector:the ith element of the vector is the number the ith term of the dictionary occurs in the document • The collection of documents is represened in the term-document matrix • Problem: the number of dimensions is too largeSolution: feature selection

7. Dimension Reduction • Feature Selection: find a subset of original variables • Document Frequency Thresholding • Omit the words with occurences greater than a threshold value, because these words are not discriminative • Omit the words with occurences less then a threshold value, because these words do not carry much information • Information gain based feature selection (information theory) • Chi-square based feature selection (statistics) • Feature Extraction: transform the data to fewer dims • Latent Semantic Indexing (LSI) • Principal Component Analysis (PCA) • Nonlinear methods

8. Latent Semantic Indexing (LSI) • SVD is applied to the term-document matrix • The features belonging to the k largest eigenvalues represent the term-document matrix well, these features are used • LSI regards documents with many common words as being semantically near

9. Principal Component Analysis (PCA) • Also called Karhunen-Loève transform (KLT) • A linear technique • Maps the data to a lower dimensional space in a way that the variance in the low-dimensional representation is maximized • The algorithm • The correlation matrix of the data is constructed • The eigenvectors and eigenvalues of the correlation matrix are calculated • The original space is reduced to the space spanned by the eigenvectors that belong to the largest eigenvalues

10. Kernel PCA • A nonlinear method • PCA + kernel trick • Kernel trick (generally) • we map observations from a general set S into a higher dimensional space V • we hope that the general classification in S reduces to the linear classification in V • the trick lets us avoid the calculation of mapping the observations from S to V • We use a learning algorithm that needs only the dot product operation in V • We use a mapping that allows to calculate the dot product within V by a kernel function K within S (the original space)

11. Manifold learning techniques • they minimize a cost function that retains local properties of the data • methods • Locally Linear Embedding (LLE) • Hessian LLE • Laplacian Eigenmaps • Local tangent space alignment (LTSA) • Maximum Variance Unfolding (MVU)

12. Locally Linear Embedding (LLE)

13. Locally Linear Embedding (LLE)

14. Maximum Variance Unfolding (MVU) • instead of defining a fixed kernel, it tries to learn the kernel using semidefinite programming • exactly preserves all pairwise distances between nearest neighbors • maximizes the distances between points that are not nearest neighbors