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.
6. Conclusion
The availability of huge volume of text based data need to be examined to extract valuable information. Text mining techniques are used to analyze the interesting and relevant information effectively and efficiently from large amount of unstructured data.
This paper presents a brief overview of text mining techniques that help to improve the text mining process. Specific patterns and sequences are applied in order to extract useful information by eliminating irrelevant details for predictive analysis.
Selection and use of right techniques and tools according to the domain help to make the text mining process easy and efficient. Domain knowledge integration, varying concepts granularity, multilingual text refinement, and natural language processing
ambiguity are major issues and challenges that arise during text mining process. In future research work, we will focus to design algorithms which will help to resolve issues presented in this work.