What is the need of Text Mining?

Text mining is also known as text analysis. It is the procedure of transforming unstructured text into structured data for simple analysis. Text mining applies natural language processing (NLP), enabling machines to know the human language and process it automatically.

It is defined as the process of extracting essential information from standard language text. Some data that it can generate via text messages, records, emails, files are written in common language text. Text mining is generally used to draw beneficial insights or patterns from such data.

Text mining is an automatic method that uses natural language processing to derive valuable insights from unstructured text. It can be converting data into information that devices can learn, text mining automates the method of defining texts by sentiment, subject, and intent.

There are two methods as Filtering and Streaming. Filtering can remove unwanted words or relevant data. Streaming words support the root for the associated words. After using the streaming method each word is defined by its root node.

The primary goals of text mining are to enable users to extract information from textbased assets and handle the operations like Retrieval, Extraction, Summarization, Categorization (supervised), and Clustering (unsupervised), Segmentation, and Association.

The main reason after the adoption of text mining is more powerful competition in the business industry, several organizations seeking value-added solutions to play with other organizations. With raising completion in business and changing user perspectives, organizations are getting huge investments to get a solution that is able of analyzing user and adversary data to improve competitiveness.

Text mining is beneficial for managing textual data. Textual data is unstructured, difficult to manipulate, and ambiguous, therefore text mining becomes the most useful method for data exchange whereas data mining is used on business data.

There are broad amounts of new records and data are created every day through economic, academic, and social activities, much with significant potential economic and societal value.

There are several techniques including text and data mining and analytics are needed to exploit this potential. The objective of this method is to reduce the efforts required for obtaining data from a huge set of textual documents.

  • Structured Data − It concerns all records which can be saved in database SQL in the table with rows and columns. They have a relational key and can be simply mapped into pre-designed fields. Today, those data are the most processed in development and the simplest method to handle information.
  • Semi-structured data − Semi-structured data is data that doesn’t include in a relational database but that have several organizational features that create it simpler to analyze. With some processes, it can save them in a relational database (it can be very difficult for some type of semi-structured data), but the semi-structure exists to ease space, certainty, or compute.
  • Unstructured data − Unstructured data describes around 80% of data. It contains text and multimedia content. It contains e-mail messages, word processing files, videos, photos, audio files, presentations, webpages, and several types of business documents.

Updated on: 15-Feb-2022


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