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
Advanced computational techniques (e.g., NLP, ML)
|Level of automation||
Automation is key. Only automated techniques are involved.
Can use both manual and automated approaches. For instance, reading is part of manual text analysis.
Relies heavily on automated knowledge extraction techniques.
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.
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)
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%.
Real-life text mining example:
The wearable tech manufacturer FitBit wanted to understand the pain points of its customers and deployed text mining tools to analyze 33,000 tweets published over a six-month period. The analysis revealed several concerns. For instance, it showed that the Fitbit Blaze product had severe issues with its operating system.
Whether in the medical field, in education, or in the legal sector, being able to “read” many research articles fast is an advantage.
For example, in the legal sector, text mining analysis can go through court cases and legal documentation helping practitioners identify case precedents and compose impactful arguments for court appearances.
In pharmaceutics, this technology can analyze biomedical research, investigating relationships between proteins, genes, diseases, etc. While in healthcare, it can look through patients’ EHRs and respond to doctors’ queries.
Real-life text mining example:
A team of researchers from the UK and Denmark applied text mining to PubMed publications’ abstracts to cluster them and identify novel drug candidates for type 2 diabetes. The team reported that this experiment helped them come up with a list of potential targets. And there is a similar study that deploys text mining algorithms to extract drug candidates for cancer treatment.
Gathering market intelligence and analyzing the competition
Text mining methods allow you to benchmark your company’s/product’s performance against the competition. As people often compare similar products from different manufacturers, you can analyze these reviews to find out where you surpassed the competition and where your product fell short.
Another way to analyze competition is deploying text mining techniques to “read” industry reports, market research articles, and press releases, which will help you stay current on what the competitors are up to.
Real-life text mining example:
A research team from China developed a text mining method that lets companies analyze textual data produced by the competition to spot different business events. The model can extract and classify events, producing each competitor’s activity sequence. This helps gauge each firm’s behavior in the market and detect any formed relationships.
Assisting in compliance management and risk mitigation
Text mining tools can continuously scan regulatory and compliance documents to help you keep your operations within the constraints of your legal landscape.
Another exciting usage of text mining is reviewing contracts for compliance with legal standards and identifying contractual risks.
Real-life text mining example:
There are several research initiatives to detect risks and compliance violations using text mining techniques. One research team deployed it to assist in calculating a manager’s fraud risk index in the financial sector. And in another example, scientists collaborated with the Youth Care Inspectorate to spot healthcare providers that pose safety risks to their patients. The team used different text mining methods to analyze over 22,000 patient complaints and detect severe violation cases.
Supporting product and service innovation
Text mining can deliver interesting and sometimes surprising ideas of how to improve your existing products or which new avenues your company can explore. In addition to the aforementioned analysis of customer support tickets, which can help you identify unmet needs, you can also use text mining algorithms to scan internal company data, such as meeting notes and brainstorming summaries, to get ideas for new products.
Yet another way is analyzing research papers and patents looking for opportunities to integrate cutting-edge tech into your products and services.
Real-life text mining example:
Before releasing a new speaker product, Amazon aimed to determine the most valuable features of competitors’ speakers in the $150 price range. The company’s data scientists deployed text mining to analyze customer reviews of the target products. They identified features that were strongly correlated with high and low speaker ratings. This not only helped Amazon build a successful product but also influenced the product launch strategy.
Challenges and limitations associated with text mining
Even though text mining is a powerful tool, there are ethical challenges and technical limitations that businesses need to be aware of before they proceed with implementation:
Quality and variety of data sources. Recent estimates show that an overwhelming 328.77 million terabytes of data is generated every day. This includes noise and irrelevant information. And even the relevant data is not standardized, which makes it difficult to create consistent rules for text processing.
Language and semantic issues. The human language is vague and complex. It includes sarcasm, polysemy, slang, and dialects. Also, add spelling mistakes to this mix. All this makes it difficult for models to work with texts. Companies will have to compose a representative dataset to train text mining algorithms to cope with all those factors.
It takes a large and diverse dataset to train text mining models. And if this data contains bias, the algorithms will produce a discriminatory outcome. Look for a reliable machine learning development vendor who can help you train and customize your models. You can also consider automated data collection to build the training set and gather data regularly in the future.
Technical and resource constraints. Some algorithms, such as NLP text analytics, require significant computational power, which makes them expensive to run. The large volumes of data can be a challenge to handle on premises. You can use the cloud for data storage and processing, which will also enable you to scale up and down painlessly.
Other technical challenges include annotating the training data, integration with existing systems, and algorithm auditing and maintenance.
Ethical and privacy concerns. Text mining might involve analyzing personal, sensitive information, such as health records. If this is the case, companies need to find a way to obtain timely consent. Ethics also influence how you use the results. If a firm got insights from biased models and deployed them in a harmful way, this would have ethical implications.
Future of text mining
Text mining algorithms are becoming smarter and more intricate. They can already give you access to the latest market intelligence and help you innovate in your production and internal operations.
With the advancements in the fields of artificial intelligence and analytics, you can combine text mining with other innovative technologies, such as generative AI. Just imagine how powerful this combination can be. Gen AI can generate content based on the insights supplied by text mining tools.
Let’s take a customer support bot as an example. Text mining techniques can extract relevant information from customer queries and supplement it with key points from FAQs and recent reviews from this customer. Gen AI takes this information and produces personalized responses addressing the client’s pain points, instead of offering some general statements that would further frustrate the person.
So, if you are already using text mining or just considering implementing this technology, maybe it’s worth to already think of integrating it with Gen AI or finding a reputable data analytics services provider to strengthen your analytical capabilities and work with real-time data.