Text Mining Techniques, Applications, and Issues
This review of current literature explores text mining techniques and industry-specific applications. Selecting and using the right techniques and tools according to the domain helps make the text-mining process easier and more efficient. As you read this article, understand this includes applying specific sequences and patterns to extract useful information by removing irrelevant details for predictive analysis. Of course, major issues that may arise during the text mining process include domain knowledge integration, varying concepts of granularity, multilingual text refinement, and natural language processing ambiguity. Figure 3 shows the inter-relationships among text mining techniques and their core functionalities. Using this as a blueprint, apply one example from your industry to each part of the Venn diagram.
4. Application of Text Mining
A. Digital Libraries
Numerous text mining techniques and tools are in use to ascertain the patterns and trends from journals and proceedings from immense amount of repositories. These sources of information help in the field of research and development. Libraries are a great
source of information for the researchers and digital libraries are endeavoring to the significance of their collection. It provides a novel method of organizing information in such a way that make it possible to available trillions of documents online.
It provides a novel way to organize information and make it possible to access millions of documents online. Green-stone international digital library that support multiple languages and multilingual interfaces provide a springy method for extracting
documents that handle multiple formats, i.e., Microsoft word, pdf, postscript, HTML, scripting languages and e-mail messages. It also supports the document extraction in the form of audio visual and image format along with text documents. In text
mining process various operation are performed like documents selection, enrichment, extracting information and tackling entities among the documents and generating instinctive co-referencing and summarization. GATE, Net Owl and Aylien are frequently
used tools for text mining in digital libraries.
B. Academic and Research Field
In education field, various text mining tools and techniques are used to analyze the educational trends in specific region, student's interest in specific field and employment ratio. Use of text mining in research field help to find and classify research
papers and relevant material of different fields at one place. The use of k-means clustering and other techniques help to identify the attributes of relevant information. Students performance in different subjects can be accessed and how different
attributes effect the selection of subjects.
C. Life Science
Life science and health care industries are generating large amount of textual and numerical data regarding patients record, diseases, medicines, symptoms and treatments of diseases and many more. It is a big challenge to filter out an appropriate and
relevant text to take a decision from a large biological repository. The medical records contain varying in nature, complex, lengthy and technical vocabulary are used that make the knowledge discovery process very difficult. Text mining tools
in biomedical field provides an opportunity to extract valuable information, their association and inferring relationship among various diseases, species, and genes. Use of an appropriate text mining tools in medical field help to evaluate the effectiveness
of medical treatments that show effectiveness by comparing different diseases, symptoms and their course of treatments. Text mining use in biomarker discovery, pharmaceutical industry, clinical trade analysis, preclinical safe toxicity studies, patent
competitive intelligence and landscaping, mapping of genes diseases and exploring the targeted identifications by using various tools.
D. Social Media
Text mining software packages are available for analyzing social media applications to monitor and analyze the online plain text from internet news, blogs, email etc. Text mining tools help to identify and analyze number of posts, likes and followers on the social media network. This kind of analysis show the people reaction on different posts, news and how it spread around. It shows the behavior of people belong to specific age group or communities having similarity and variation in views about the same post
E. Business Intelligence
Text mining plays a significant role in business intelligence that help organizations and enterprises to analyze their customers and competitors to take better decisions. It provides a deeper insight about business and give information how to improve
the customer satisfaction and gain competitive advantages. The text mining tools like IBM text analytics, Rapid miner, GATE help to take decisions about the organization that generate alerts about good and bad performance, market changeover that help
to take remedial actions. It also helps in telecommunication industry, business and commerce applications and customer chain management system.