What is Text Mining: Techniques and Applications

Text Mining is one of the most critical ways of analysing and processing unstructured data which forms nearly 80% of the world’s data. Today a majority of organisations and institutions gather and store massive amounts of data in data warehouses, and cloud platforms and this data continues to grow exponentially by the minute as new data comes pouring in from multiple sources. As a result, it becomes a challenge for companies and organisations to store, process, and analyse vast amounts of textual data with traditional tools. This is where Text Mining comes in.  

What is Text Mining?

According to Wikipedia, “Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text.” The definition strikes at the primary chord of text mining – to delve into unstructured data to extract meaningful patterns and insights required for exploring textual data sources.

Text mining incorporates and integrates the tools of information retrieval, data mining, machine learning, statistics, and computational linguistics, and hence, it is nothing short of a multidisciplinary field. Text mining deals with natural language texts either stored in semi-structured or unstructured formats.
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The five fundamental steps involved in text mining are:

  • Gathering unstructured data from multiple data sources like plain text, web pages, pdf files, emails, and blogs,  to name a few.
  • Detect and remove anomalies from data by conducting pre-processing and cleansing operations. Data cleansing allows you to extract and retain the valuable information hidden within the data and to help identify the roots of specific words.
  • Convert all the relevant information extracted from unstructured data into structured formats.
  • Analyse the patterns within the data via Management Information System (MIS).
  • Store all the valuable information into a secure database to drive trend analysis and enhance the decision-making process of the organisation.

text mining

Text Mining Techniques

Let us now look at the most famous techniques used in text mining techniques:

  1. Information Extraction

Information Extraction (IE) refers to the process of extracting meaningful information from vast chunks of textual data. This method focuses on identifying the extraction of entities, attributes, and their relationships from semi-structured or unstructured texts. Whatever information is extracted is then stored in a database for future access and retrieval.  The efficacy and relevancy of the outcomes are checked and evaluated using precision and recall processes.

  1. Information Retrieval

Information Retrieval (IR) refers to the process of extracting relevant and associated patterns based on a specific set of words or phrases. IR systems make use of different algorithms to track and monitor user behaviors and discover relevant data accordingly. Google and Yahoo search engines are the two most renowned IR systems.
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  1. Categorisation

Text categorisation is a form of “supervised” learning wherein normal language texts are assigned to a predefined set of topics depending upon their content. Thus, categorisation or rather Natural Language Processing (NLP) is a process of gathering text documents and processing and analysing them to uncover the right topics or indexes for each document. The co-referencing method is commonly used as a part of NLP to extract relevant synonyms and abbreviations from textual data. Today, NLP has become an automated process used in a host of contexts ranging from personalised commercials delivery to spam filtering and categorising web pages under hierarchical definitions, and much more.

  1. Clustering

Clustering is one of the most crucial techniques of text mining. It seeks to identify intrinsic structures in textual information and organise them into relevant subgroups or ‘clusters’  for further analysis. A significant challenge in the clustering process is to form meaningful clusters from the unlabeled textual data without having any prior information on them. Cluster analysis is a standard text mining tool that assists in data distribution or acts as a pre-processing step for other text mining algorithms running on detected clusters.

  1. Summarisation

Text summarisation refers to the process of automatically generating a compressed version of a specific text that holds valuable information for the end user. The aim here is to browse through multiple text sources to craft summaries of texts containing a considerable proportion of information in a concise format, keeping the overall meaning and intent of the original documents essentially the same. Text summarisation integrates and combines the various methods that employ text categorisation like decision trees, neural networks, regression models, and swarm intelligence.
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Applications Of Text Mining

Text mining techniques are rapidly penetrating the industry, right from academia and healthcare to businesses and social media platforms. Here are a few applications of text mining being used across the globe today:

  1. Risk Management

One of the primary causes of failure in the business sector is the lack of proper or insufficient risk analysis. Adopting and integrating risk management software powered by text mining technologies such as SAS Text Miner can help businesses to stay updated with all the current trends in the business market and boost their abilities to mitigate potential risks. Since text mining technologies can gather relevant information from across thousands of text data sources and create links between the extracted insights, it allows companies to access the right information at the right moment, thereby enhancing the entire risk management process.

  1. Customer care service

Text mining techniques, particularly NLP, are finding increasing importance in the field of customer care. Companies are investing in text analytics software to enhance their overall customer experience by accessing the textual data from varied sources such as surveys, customer feedback, and customer calls, etc. Text analysis aims to reduce the response time of the company and help address the grievances of the customers speedily and efficiently.

  1. Fraud Detection

Text analytics backed by text mining technologies provides a tremendous opportunity for domains that gather a majority of data in the text format. Insurance and finance companies are harnessing this opportunity. By combining the outcomes of text analyses with relevant structured data these companies are now able to process claims swiftly as well as detect and prevent frauds.

  1. Business Intelligence

Organisations and business firms have started to leverage text mining techniques as part of their business intelligence. Apart from providing profound insights into customer behaviour and trends, text mining techniques also help companies to analyse the strengths and weaknesses of their rivals, thus, giving them a competitive advantage in the market. Text mining tools such as Cogito Intelligence Platform and IBM text analytics provide insights on the performance of marketing strategies, latest customer and market trends, and so on.  

  1.  Social Media Analysis

There are many text mining software packages designed exclusively for analysing the performance of social media platforms. These help to track and interpret the texts generated online from the news, blogs, emails, etc. Furthermore, text mining tools can efficiently analyse the number of posts, likes, and followers of your brand on social media, thereby allowing you to understand the reaction of people who are interacting with your brand and online content. The analysis will enable you to understand ‘what’s hot and what’s not’ for your target audience.
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We hope this informative piece helped you understand the basic of text mining and its applications in the industry.

Abhinav Rai

Abhinav is a Data Analyst at UpGrad. He's an experienced Data Analyst with a demonstrated history of working in the higher education industry. Strong information technology professional skilled in Python, R, and Machine Learning.
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