One of the most efficient and organized ways of processing and evaluating unstructured data (which is nearly 80% of the world’s data) is Text Mining. Huge amounts of data is collated and stored on cloud platforms and data warehouses and it is a challenge to continue to store, process, and evaluate such huge amounts of data by using only traditional tools. This is exactly where text mining can help.

Text mining is the process of gathering high-quality information from unstructured text.

Steps Involved in Text Mining:

  1. To gather unstructured data from numerous data sources such as Web pages, pdf files, blogs, e-mails and so on.
  2. To identify and delete data variations by performing pre-processing and cleansing operations. For this, there are a number of text mining applications and tools available.
  3. To convert all the appropriate data pulled out from unstructured data into structured formats.
  4. To evaluate the patterns within the data through the Management Information System (MIS).
  5. To collate and store the valuable information into a secure database.


  1. Information Extraction: This technique concentrates on detecting the extraction of attributes, entities, and their relationships from unstructured or semi-structured texts. Information extracted is then kept in a database for access and retrieval as and when required.
  2. Information Retrieval: Information Retrieval (IR) refers to extracting appropriate and related patterns based on a specific set of phrases or words. Yahoo and Google search engines are examples of IR systems.
  3. Categorization: This technique is a form of supervised learning where normal language texts are allocated to a predefined set of topics based on their content. Thus, categorization or rather Natural Language Processing (NLP) is a method of collating text documents, evaluating, and processing them to extract the relevant indexes or topics for each document.
  4. Clustering: One of the most vital text mining techniques, this process classifies intrinsic structures in textual information and then organizes them into appropriate subgroups or clusters for detailed analysis.
  5. Summarization: This technique comprises of automatically creating a compressed version of a text that is relevant to a user. Thus, the aim is to browse through various text sources to create and design summaries of texts containing appropriate information in a short format, while retaining the overall meaning of the documents. The various methods used for this technique are neural networks, decision trees, regression models, and swarm intelligence.


Following are a few applications of text mining used across the globe:

  • Risk Management: One of the prime causes of business failures is insufficient risk analysis. Implementing and integrating risk management software which are powered by text mining technologies such as SAS Text Miner can help organizations to remain updated with the current trends in the business market and increase their capacities to evade potential risks.
  • Customer Care Service: Text mining techniques, such as NLP, have marked their reputation in the customer care field. Organizations now invest in text analytics software to improve their customer experience by retrieving the textual data from various sources such as customer feedback, surveys, and customer calls. Text analysis reduces the company response time and helps in addressing customer grievances efficiently.
  • Fraud Detection: Text analytics along with the various text mining techniques offers an incredible opportunity by merging the outcomes of text analyses with appropriate structured data.
  • Business Intelligence: Text mining techniques help organizations to identify the strengths and weaknesses of competitors. Text mining tools such as IBM text analytics and Cogito Intelligence Platform offer insights on the performance of new customer and market trends, marketing tactics, and so on.
  • Social Media Analysis: Various text mining tools are designed exclusively for evaluating the performance of social media platforms. These tools help to interpret and track the texts generated online from blogs, news, blogs, e-mails, and so on. Furthermore, text mining tools can efficiently evaluate the number of likes, posts, and followers of one’s brand present on social media, thereby helping to understand ‘what’s hot and what’s not’ for the audience.