Text Mining: Techniques, Applications and Issues
| Autor: | Muhammad Kashif Hanif, Shaeela Ayesha, Fakeeha Fatima, Ramzan Talib |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Information retrieval
General Computer Science business.industry Data stream mining Computer science Concept mining 02 engineering and technology computer.software_genre Data science Field (computer science) Information extraction Text mining Knowledge extraction 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Relevance (information retrieval) business Co-occurrence networks computer |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 7 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2016.071153 |
| Popis: | Rapid progress in digital data acquisition tech-niques have led to huge volume of data. More than 80 percent of today’s data is composed of unstructured or semi-structured data. The discovery of appropriate patterns and trends to analyze the text documents from massive volume of data is a big issue. Text mining is a process of extracting interesting and non-trivial patterns from huge amount of text documents. There exist different techniques and tools to mine the text and discover valuable information for future prediction and decision making process. The selection of right and appropriate text mining technique helps to enhance the speed and decreases the time and effort required to extract valuable information. This paper briefly discuss and analyze the text mining techniques and their applications in diverse fields of life. Moreover, the issues in the field of text mining that affect the accuracy and relevance of results are identified. |
| Databáze: | OpenAIRE |
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