Businesses and economies across the globe rely on computer networks, this has resulted in the continuous growth of machine-readable documents. The information contained within these documents is in the form of unstructured text. Text Mining is used to extract useful information from unstructured text.
In knowledge-driven industries such as life sciences & healthcare, it is important to find the right information quickly from large volumes of textual data. Decisions are based on this information and for the best decisions right and quick information is required.
More than 80% of the information is available in the unstructured text. The conventional keyword search mechanism only helps you retrieve the documents, which still someone needs to read through one by one. This is way much time consuming and a lot gets missed out in this approach.
On the other hand, Text Mining is not only faster, but it also unleashes important information that would otherwise remain hidden. Text Mining analyses the meaning of the text by using linguistic algorithms and enables you to ask open questions, find the relevant facts, and identify valuable connections.
Text Mining is far superior to a simple keyword search and it recognizes concepts in different ways and can express it in a much useful manner like high-quality results comprised of structured information/knowledge which is actionable and enables you to quickly review and conduct analysis and gain speedy insights.
Text Mining primarily deals with two things.
1) Unstructured Text .
2) Extraction of useful information.
Text mining process has three different perspectives;
1) Text Mining as Information Extraction
2) Text Mining as Data Mining
3) Text Mining as KDD (Knowledge Discovery in Databases)
Text Mining is the knowledge discovery from the text (KDT). It deals with the machine supported analysis of the text. It uses techniques from information retrieval, information extraction as well as natural language processing (NLP) and connects them with the algorithms and methods of KDD, data mining, machine learning, and statistics. Text documents are in the focus of the analysis.
Text Mining = Information Extraction
Text Mining gives the ability to process unstructured text in a very large set of documents usually in thousands or millions. It interprets the meaning and automatically identifies an extract out concepts and relationship between those concepts to directly answer the questions of interest.
Areas Of Text Mining
Process of Text Mining
Text Transformation is a technique to control the capitalization of the text.
Text Pre-Processing is used for extracting useful information and knowledge from unstructured text.
Feature Selection or Variable Selection is a process to reduce the input of processing or finding the essential information sources.
In Data Mining the classic data mining methods are used in the structural database.
Next, the results are evaluated.
Applications To Which Text Mining Is Their Core
Risk Management
Risk Management applications or software are widely used by financial organizations. To analyze, identify, evaluate, treat, and monitor risk, the Risk Management Applications based on Text Mining ability is utilized. It makes it possible to analyze millions of text documents and extract the most needed information at the right time.
Customer Care Service
Text Mining methods are largely being used by organizations to improve customer care. Textual data is accessed from different sources like customer feedback, surveys, customer calls, etc. and is evaluated and analyzed to take effective measures to improve response time overall customer care.
Business Intelligence
Text mining is becoming a major partner to Business Intelligence. Businesses and large organizations are using Text Mining couple with BI to analyze the strengths and weaknesses of their competitors, this is helping them to stay competitive in the markets.
Social Media Analysis
Text Mining tools are used to track online data. Text Mining tools have the ability to track the total number of posts, likes, dislikes, etc. This helps you understand customer behavior towards your brand, and you can take measured decisions based on this.
Conclusion
Text Mining is widely used in knowledge-driven organizations. Text Mining identifies facts, relationships, and assertions. This information after the extraction part is made available in the structured form that gets further analyzed or can be dished out in different formats like HTML Tables, Maps, etc.
The structured data created by text mining can be integrated into databases, data warehouses, or business intelligence dashboards and used for descriptive, prescriptive, or predictive analytics.
Text mining requires NLP as its core element to complete the process.
If you have any text mining or other related unstructured data needs, please feel free to Contact Us.
