Text Mining and its Applications

1. Front Matter

   Pages I-X

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2. Mining for Gems of Information

   • Spiros Sirmakessis

   Pages 1-6

3. From Text to Knowledge: Document Processing and Visualization: a Text Mining Approach

   • Martin Rajman, Martin Vesely

   Pages 7-24

4. Web Mining: Past, Present and Future

   • Konstantinos Markellos, Penelope Markellou, Maria Rigou, Spiros Sirmakessis

   Pages 25-35

5. Applications, Sectors and Strategies of Text Mining, a First Overall Picture

   • Sergio Bolasco, Francesco Baiocchi, Alessio Canzonetti, Francesca Della Ratta, Ana Feldman

   Pages 37-51

6. Text Classification of News Articles with Support Vector Machines

   • Gerhard Paaß, Jörg Kindermann, Edda Leopold

   Pages 53-64

7. A Review of Web Document Clustering Approaches

   • N. Oikonomakou, M. Vazirgiannis

   Pages 65-79

8. Supervised Term Weighting for Automated Text Categorization

   • Franca Debole, Fabrizio Sebastiani

   Pages 81-97

9. Machine Learning for Information Extraction in Genomics — State of the Art and Perspectives

   • C. Nédellec

   Pages 99-118

10. Processing Multilingual Collections for Text Mining Applications

    • Eric Gaussier

    Pages 119-130

11. Text Mining Tools: Evaluation Methods and Criteria

    • Antonis Spinakis, Paraskevi Peristera

    Pages 131-149

12. Knowledge Advantage through online Text Mining. Research Trends in Competitive Intelligence and Virtual Communities Applications

    • Alessandro Zanasi

    Pages 151-157

13. Real Time Customer Opinion Monitoring

    • Luca Dini, Giampaolo Mazzini

    Pages 159-167

14. Validation Techniques in Text Mining (with Application to the Processing of Open-ended Questions)

    • Ludovic Lebart

    Pages 169-178

15. Clickstream Analysis, Semiotic Interpretation and Semantic Text Mining for a Distance Measurement on the Hypertextual Map of an Internet-portal

    • Furio Camillo

    Pages 179-188

16. Text Mining in Official Statistics

    • Mónica Becue, Bert Fridlund, Alf Fyhrlund, Albert Prat, Bo Sundgren

    Pages 189-204

17. Back Matter

    Pages 204-204

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The world of text mining is simultaneously a minefield and a gold mine. It is an exciting application field and an area of scientific research that is currently under rapid development. It uses techniques from well-established scientific fields (e.g. data mining, machine learning, information retrieval, natural language processing, case­ based reasoning, statistics and knowledge management) in an effort to help people gain insight, understand and interpret large quantities of (usually) semi-structured and unstructured data. Despite the advances made during the last few years, many issues remain umesolved. Proper co-ordination activities, dissemination of current trends and standardisation of the procedures have been identified, as key needs. There are many questions still unanswered, especially to the potential users; what is the scope of Text Mining, who uses it and for what purpose, what constitutes the leading trends in the field of Text Mining -especially in relation to IT- and whether there still remain areas to be covered.

• Hypertext
• case-based reasoning
• classification
• clustering
• data mining
• genome
• intelligence
• knowledge
• knowledge management
• learning
• machine learning
• statistics
• text mining
• visualization
• web mining
• information and communication, circuits

• Computer Technology Institute, Research Academic, Patras, Greece

  Spiros Sirmakessis

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