- 24th September 2025
- Posted by: Breige McBride
- Category: Bioinformatics
What is Text Mining?
Also known as ‘text data mining’, text mining transforms unstructured data into a structured, analysable dataset. While structured data is organised in tabular formats of rows and columns, unstructured data does not have a predefined format. Examples of text-based unstructured data include text from emails, chat logs, social media posts and documents. There are also non-text based sources of unstructured data, such as images, videos and audio files.
Every day, massive amounts of unstructured data are generated and many valuable insights are buried within all that information. However, uncovering those insights requires transforming that unstructured data into structured data that can be analysed. Text mining employs a variety of analysis methodologies to achieve this, including information extraction, information retrieval, clustering, categorisation, text summarisation and natural language processing (NLP).
Since roughly 80% of the world’s data exists in an unstructured format¹, text mining is an essential data analysis tool. It is already widely used in various industries to uncover valuable insights from sources that were previously too complex or time-consuming to analyse manually. Let’s take a look at some of these real-world applications of text mining.
Text Mining Applications
Customer Service
When companies use chatbots or surveys, they collect large volumes of customer feedback daily. Text mining can help to identify common issues and detect customer sentiment, enabling teams to resolve problems more efficiently and enhance overall customer satisfaction.
Risk Management
Text mining can support risk management. For example, in the finance industry it can be used to scan analyst briefings, reports, and news to identify emerging risks and opportunities. This enables companies to make more confident investment and risk decisions.
Maintenance and Operations
For industries reliant on equipment and machinery, text mining can be a game-changer. Companies can use it to analyse logs, records, and reports to find patterns linked to faults or failures. Over time, this enables predictive maintenance, reducing downtime and helping prevent breakdowns.
Cybersecurity & Spam Filtering
Spam emails are more than just annoying; they can be dangerous. Text mining techniques are used to detect and filter out spam by recognising language patterns commonly used in phishing or malware attacks, improving inbox security and protecting users from cyber threats.
Healthcare and Research
Medical researchers face a continual flood of new research publications and data. However, they can use text mining to speed up the process of sifting through medical literature, to uncover relevant studies, biomarkers, and other information relevant to their research goals. In fact, this is something we regularly help our pharma, biotech and academic clients with at Fios Genomics. We do this mainly via Natural Language Processing (NLP), which is one of the key analysis methodologies of text mining.
Natural Language Processing
NLP helps machines to understand, interpret, and generate human language. The aim of NLP is to bridge the communication gap between humans and computers by making it easier to interact using natural language. It is used in cases when it is not feasible to read all the information ourselves to identify what’s most important. Rather than working like a search tool that returns a list of sources that match the search request, text mining goes further. It provides detailed information about the text itself. For example, it can understand text meanings, detect tone, emotion, and complex relationships in large volumes of unstructured text. This makes it ideal for analysing data from scientific publications, clinical records, social media posts, or customer feedback, to uncover valuable insights and patterns that could otherwise go unnoticed.
Core Components of NLP
- Part-of-Speech (PoS) tagging: Allocates a tag for each word or token in a document, to assign it a grammatical category such as noun, verb, adjective etc.
- Sentiment analysis: Determines positive, negative or neutral sentiment
- Summarisation: Distilling long documents into key points for improved information extraction
- Text categorisation: categorises documents according to predefined topics or categories
Discover Key Insights with Text Mining from Fios Genomics
At Fios Genomics, we use NLP to quickly uncover critical insights from text data that is either supplied by you or gathered from the public domain, to further your research. With your research goal in mind, we can perform a data landscaping exercise to identify relevant publications to apply NLP to, to discover valuable insights to further your research. For example, we can use data landscaping to identify medical and scientific research publications containing co-occurrences between specific drug targets and terms related to disease phenotypes or adverse events, such as “liver toxicity.” Then, by applying modern natural language processing techniques, we can extract and statistically weight those associations to prioritise drug targets, uncover novel risk relationships, and inform safer drug development decisions.
This is just one way we can use NLP. To find out how we can use it to advance your research get in touch to have a free, no-obligation exploratory discussion.
However, if you are unsure if text mining is the right fit for your research, you can take a look at some of its key benefits, below.
Discover Benefits of Text Mining
Maximises Existing Data
Text mining enables you to make the most of your data, facilitating the discovery of key insights that may have been missed simply due to the scale and volume of the information. It can also eliminate the need to generate your own data. Vast quantities of data are already available in the public domain, and text mining allows you to make the most of this available data, by mining it for insights to advance your research.
Validates In-House Results
Before investing in new experiments, it’s wise to check whether existing data supports your findings. Text mining offers a reliable way to validate your results and ensure you’re heading in the right direction.
Supports Hypothesis Generation
By uncovering hidden connections and trends across complex datasets, text mining can spark novel hypotheses that shape your next research question or support the design of your follow-up experiments.
Enables Faster Discoveries
Text mining streamlines the information-gathering phase of research, enabling you to make discoveries faster and with greater confidence, ultimately making your research process more efficient.
Interested in Text Mining?
If you are interested in how text mining can further your research, complete the form below and one of our bioinformatics specialists will get in touch to discuss your research and how text mining can help.
Author: Coco Goto-Colverson, Marketing Intern, Fios Genomics
Reviewed by Fios Genomics Bioinformatics Experts to ensure accuracy
Sources
- Dialani, P. (2020). The Future of Data Revolution will be Unstructured Data. [online] Analytics Insight. Available at: https://www.analyticsinsight.net/insights/the-future-of-data-revolution-will-be-unstructured-data.
See also:
Optimising Preclinical Drug Development with Data Mining
Why do Gene Therapies Cost so Much?
Bioinformatics Consulting: 5 Reasons to Choose Fios Genomics
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