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.
Abstract
Rapid progress in digital data acquisition techniques 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 nontrivial 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.
Keywords – Classification; Knowledge Discovery; Applications; Information Extraction; Patterns
Source: Ramzan Talib, Muhammad Kashif Hanif, Shaeela Ayesha, and Fakeeha Fatima, https://thesai.org/Publications/ViewPaper?Volume=7&Issue=11&Code=ijacsa&SerialNo=53
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