Media and Text Mining

1. Problems in Analytical Business Intelligence by multinational companies. Metadata systems and standards: DC, RDF, MPEG-7.
2. Typical task types in Media and Text Mining. Search, classification, clustering, forecasting and their combinations.
3. Methods for media and text analysis, search techniques, indexing, ranking procedures. Bag of words model.
4. Searching on he Web, Web Mining. PageRank, webgraph methods, HITS, Boole-search, weighting schemes (tf-idf, etc.), cosines distance.
5. Dimension reduction methods, feature extraction and feature selection techniques, chi-square, eigenvalue based methods, independent component analysis (ICA).
6. Classification of pictures, videos. Discretization. Types and methods of media classification. Support vector machine for media classification.
7. Text analysis. Stemming algorithms, Porter stemmer, Lovins stemmer. Language detection, language dependency. Shallow and deep parsing. POS tagging. Syntax tree parsers, dependency graph parser. Stanford tools.
8. Text classification. Types and methods of text classification. Gini index. C4.5, C5.0, Random Forest. Automatic text processing at enterprises.
9. Text and media clustering. Various distance measure. Agglomerative and divisive clustering. Hierarchical clustering (bottom-up and top-down), k-means clustering, density-based clustering.
10. Relation extraction from text. Co-occurrence, pattern-matching and supervised learning methods. Convolution kernels with SVM in relation extraction. Gathering business news, information extraction from the news.
11. Hierarchical taxonomy systems, Catalogue search, thesaurus. Folksonomy, methods for multiusers. Concept mining. Annotation. Sentiment analysis.
12. Context-Based Image Retrieval. Line detection, skeletonization. Image and time series in multimedia.
13. Media-indexing. Probability models in video and audio searches. Applications of Hidden Markov Models.
14. Developing media retrieval and search systems in enterprises. Marketing applications, online media applications.

Laboratory:

1. The tasks should be solved by data mining and text mining softwares (e.g. SAS software modules)
2. Searching techniques in a predefined corpus.
3. Media classification exercises.
4. Picture clustering exercises.
5. Text analysis.
6. Text categorization.
7. Context-Based Image Retrieval with a large set of pictures.
8. Lift diagram analysis