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What is Text Mining? What are its Techniques and Applications?

In laymen terms, text mining is a process to delve into the unstructured data to extract meaningful patterns and insights from it in order to explore the textual data sources. Text mining indulges and integrates the information retrieval tools, data mining, machine learning, statistics, and computational linguistics, and therefore is nothing short of a multidisciplinary field. Text mining is associated with natural language texts that are either stored in semi-structured or unstructured formats.

The fundamental steps involved in data mining are as follows:

  1. Soliciting unstructured data from various data sources such as plain text, web pages, pdf files, emails, blogs, and the like.
  2. Detect and get rid of anomalies from data by integrating pre-processing and cleansing operations. Data cleansing lets you extract and retain the important information hidden within the data and to assist the identification of the roots of specific words.
  3. Translate all the relevant information extracted from unstructured data into structured formats.
  4. Pattern analysis within the data through Management Information System or MIS.
  5. Store all the important information into a secure database to drive trend analysis and improvise the decision-making process of the organization.

Text Mining Techniques

  1. Information Extraction or IE

It refers to the process of extracting valuable information from the huge chunk of data. This method centralizes on the extraction of the attributes, and their relationships from semi-structured or unstructured texts. No matter what information is filtered out, it is then further stored in a database for future reference, access, and retrieval. The relevancy and efficacy of the results are verified and evaluated by precision and recall processes.

  1. Information Retrieval or IR

It refers to the process of filtering out relevant and associated patterns on the basis of a particular set of words or phrases. IR systems utilize many kinds of algorithms in order to track and monitor user behaviors and explore the relevant data accordingly. The two most renowned IR systems are Google and Yahoo! search engines.

  1. Categorization

It is a kind of “supervised” learning where the normal language texts are given to a predefined set of topics relying upon their content it has. Hence, categorization or Natural Language Processing (NLP) is a process of soliciting text documents, processing and analyzing them to unleash the correct topics or indexes for each and every document. The co-referencing method is generally used by Provalis Research as a part of NLP to filter out relevant synonyms and abbreviations from textual data. Now, NLP has become an automated process which is used in a host of contexts ranging from personalized commercials delivery to spam filtering and grouping web pages under hierarchical definitions, and much more.

  1. Clustering

It focuses on identifying the intrinsic structures in textual information and group them into relevant subgroups or ‘clusters’ for future analysis. It is a standard text mining tool that helps in distributing the data or acts as a pre-processing step for other text mining algorithms running on the targeted clusters.

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