What Is Text Mining, and How Does It Enable Businesses to Benefit from Unstructured Data?

Unstructured data accounts for 80% – 90% of all new data generated by enterprises, and text mining is the technique that will help you put it to use.

Many businesses can already manage their structured data, but what about the insights hiding in free-format text?

Unstructured data is the data that doesn’t fit neatly into a database or a spreadsheet, making it impossible for traditional analytics tools to process. This is when companies turn to NLP solution providers and other advanced technology vendors to capitalize on this opportunity.

So, what is text mining? And how can you deploy it within your business settings?

Text mining definition and business benefits

What is text mining?

Text mining is the process of extracting valuable insights from large amounts of unstructured textual data. This is equivalent to teaching a computer to read and analyze texts, just like humans, but much faster and on a larger scale.

Text mining allows you to tap into a wide range of unstructured data, including social media posts, product review pages, research reports, emails, and more, without the need to manually review the original texts. As a result, you will be aware of any emerging concerns prior to escalation and will recognize upcoming trends before your competition.

Text mining vs. text analysis vs. text analytics

Many professionals use the terms text mining and text analysis interchangeably, and this is correct in many cases. However, there are subtle differences between the two concepts.

The main issue is that text mining focuses on automated pattern discovery and knowledge extraction, while text analysis uses a broader range of techniques to interpret and examine textual data. It deals with language recognition, summarization, categorization, etc. It’s safe to say that text mining is a subtype of text analysis, which focuses on automated pattern discovery.

Text analytics uses both text mining and analytics techniques to process textual data. Text mining has more of a qualitative nature, while text analytics focuses on creating graphs and other data visualizations, making it more of a quantitative tool.

The scope of all three concepts overlaps, and they often rely on the same techniques to accomplish slightly different goals, blurring the distinction among them.

	Text mining	Text analysis	Text analytics
The primary goal	Analyzing text to discover relevant information and insights	Examining and interpreting textual data	Finding patterns and trends in texts and (often) creating visualizations
Methods used	Machine learning NLP Statistical analysis	Computational methods (e.g., NLP) Non-computational methods (e.g., content analysis)	Advanced computational techniques (e.g., NLP, ML)
Level of automation	Automation is key. Only automated techniques are involved. There is still some degree of human intervention at the feature selection, design, and validation stages, while the techniques run automatically.	Can use both manual and automated approaches. For instance, reading is part of manual text analysis.	Relies heavily on automated knowledge extraction techniques. The human intervention level is the same as in text mining.

To have a better understanding of the concepts despite their overlap, let’s see what each of the three techniques can do in the context of customer feedback analysis:

Text mining can extract patterns from a large dataset of thousands of unstructured client reviews. It can deploy ML to identify frequently mentioned concerns and common themes of these reviews.
Text analytics can also analyze large volumes of reviews. It can deploy ML and sentiment analysis tools to generate a structured report on the prevailing sentiment and any potential risks that your business needs to address.
Text analysis can perform an in-depth study of several selected customer reviews. It would analyze each review in detail to understand any concerns and suggestions. This technique can report on a detailed customer experience.

Text mining benefits

Enhances your decision-making skills. Text mining algorithms transform texts into actionable insights that can help executives solve pressing business problems.
Gives you competitive intelligence. You can analyze market trends, your competitors’ news and activities, and see what customers think of their products and marketing campaigns. This enables you to gauge the market dynamics, spot early opportunities, and capitalize on them before your competition.
Spots risks and helps you manage them. You can deploy these techniques to search for anomalies, demand fluctuation, and other issues that might threaten your business. Text mining can also detect early signs of fraud, cyberattacks, and compliance violations.
Quickly analyzes unmanageably large texts. To give you an idea of text mining speed, it can go through a 400-page book in a matter of minutes to perform a task like simple pattern recognition – provided the algorithm is optimized and sufficient computational resources are allocated. Sophisticated linguistic analysis can take hours, which is still much faster than the human pace.

How text mining works

Text mining relies on a variety of techniques to extract insights from free-form texts and present the findings in a structured format.

Machine learning (ML) is the foundational technology for many of these methods, as it can automatically learn patterns for text extraction, classification, and clustering. In addition to ML, text mining can use statistical approaches, rule-based methods, and linguistic analysis.

This graph demonstrates how text mining works.

Text mining techniques

Here are some examples of text mining techniques, which can be ML-powered.

Information retrieval

Text mining tools receive a query and search for specific information in a heap of text and retrieve the desired piece of data. For instance, information retrieval methods are deployed in search engines, such as Google, and in library cataloging systems.

Here are the key subtasks that assist in information retrieval:

Tokenization breaks down long texts into individual units – i.e., tokens – which can be individual words, sentences, or phrases.
Stemming reduces the word to its root form, removing suffixes and prefixes.

Information extraction (IE)

Information extraction is about retrieving structured information from free-form text. These techniques can extract entities of interest, their relationships, and attributes and organize them in an easy-to-access format.

One application of IE is market trends extraction from news articles. The models can scan the news section and pull out competitors’ names, financial information, product mentions, etc., and present this data in a structured manner.

Here are the common IE subtasks:

Feature selection depicts the important attributes
Feature extraction further granulates the task by extracting a subset of each relevant feature
Named-entity recognition identifies entities, such as peoples’ names, locations, etc. in text

Natural language processing (NLP)

This is an advanced technique that relies on artificial intelligence, linguistics, and data science, among other methods. NLP text mining enables machines to “understand” human language.

For instance, NLP can come in handy if you want to know how customers feel about the new product/service that you released recently. You will need a tool that can go through large volumes of product/service feedback published on different platforms.

Here are the most common natural language processing text mining subtasks:

Summarization. This technique supplies you with a concise summary of long reads, be it large articles or even books.
Text categorization. Also known as text classification, this method assigns labels to unstructured data. For instance, it can categorize text documents into predefined categories, or classify customer reviews based on products they mention.
Sentiment analysis. Put simply, sentiment analysis and text mining can identify positive, neutral, and negative sentiments in text. It lets you track people’s attitudes towards your brand over time, like in the NLP example above. You can find more information on AI-powered sentiment analysis on our blog.

Text mining applications in the business world

By incorporating text mining solutions into your company’s tech stack, you can unlock the following:

Anticipating customers’ needs and offering better support

You can use text mining techniques to analyze customer feedback from social media, surveys, and other sources, understand what people like about your product or service, and look for tips that can help you align your offering with customer expectations.

You can also increase the efficiency of your customer support operations by analyzing support tickets, chats, and even lengthy transcriptions of support calls. This enables your team to categorize outstanding issues and identify urgent matters to provide better customer service.

McKinsey reports that applying advanced text analytics can decrease call handling time by 40% while increasing conversion rates by around 50%.