Text mining – possibilities and business applications

Data analytics of all kinds is a must today – it is the foundation for development, process improvement, and the ability to align a business with the actual needs and sentiments of the market or the company’s own employees. Usually, when we think of “data analytics,” we imagine formulas, numbers, and system data; however, many written texts also contain essential data – but how do we extract it? This is where the technology of text mining – text exploration – comes in, which involves extracting data from text, organizing it, processing it, and performing final analysis. How can text mining help us, and what is it useful for?    

Text mining – what is it and what opportunities does it bring to business?

  On our blog, we have already mentioned a similarly named technology, Task Mining – a new technology that allows companies to capture data on user interactions. This brings many benefits, as it monitors employee actions in real time (clicks, scrolling, and other activities) while simultaneously pointing out areas that can be improved. The topic we want to address in this article is text mining – however, it relies on something completely different than task mining (aside from the general category of “data exploration”). In this case, exploration and analysis concern text – specifically unstructured text that is organized, allowing us to identify specific patterns, high-quality information, and new, important insights. Data in the form of text fields is everywhere, in files of various formats – these include articles, posts on specialized forums, comments, documents, correspondence (e.g., email, chat, etc.), books, and more. Text mining is a technology that uses artificial intelligence (AI – artificial technology) and natural language processing (NLP – natural language processing, also used in technologies such as cRPA

) to understand human language and obtain relevant results. Proper preparation of all data is necessary so that it can be analyzed automatically and so that a computer can understand and correctly read it. We can use relational databases for this, for example. The prepared and structured data is then passed to an analytical system, which, using algorithms and NLP (for example, in the Python programming language), can derive high-quality information that is useful to us—information that might be impossible, or at least very costly and time-consuming, to extract through human analysis.    

Text mining and big data – can they be combined?   First, let’s answer the question of what big data is – these are data sets too large and complex for traditional applications or data processing software to handle. Big data analytics involves using more advanced technologies capable of managing these massive datasets, which contain fully or partially structured and unstructured data from various sources. This data is becoming increasingly diverse and complex because it is generated in real time and on a large scale through the use of AI (artificial intelligence), mobile and stationary devices, social media, video/audio, online transactions, sensors, and more. Can we combine big data and text mining? Technological progress allows us to do so; as mentioned in the publication “Text Mining in Big Data Analytics”, this is a still-evolving topic that faces challenges such as understanding natural language from very different sources or misinformation (for example, on social media). Nevertheless, we can already see the enormous possibilities and benefits of applying text mining in big data analytics. Science, business, and development across many industries and areas – medicine, education, public health, and the prevention of social phenomena such as crime, bullying, or poverty – text mining

is capable of providing a wealth of information and conclusions about both society as a whole and individuals, which will benefit the aforementioned fields and topics.    

  What are some examples of text mining applications in business?Text mining works great in business if we deal directly with customers and care about their comfort and service quality, which relates to general interest in customer experience (CX – the perception of a company or brand by the customer based on their impressions and the sum of all customer experiences at all touchpoints with the company throughout the entire customer journey). Important information that we can analyze using text mining technology is obtained from texts such as customer responses to satisfaction surveys (e.g., NPS – Net Promoter Score), tickets reported by them describing problems they have with our products, meeting notes, or social media comments. Chatbots on websites also use text exploration and analysis, as they must automatically, quickly, and consistently answer a question or statement sent by a customer through a chat window. Thanks to text mining technology, we can also better protect our companies against cyberattacks and spam – real threats are detected more easily because we have more context and examples in the database, and algorithms and NLP are able to catch them. A human will not be as fast or “knowledgeable” about what to look for in suspicious email correspondence, whereas text mining technology, supported by artificial intelligence, will do it very efficiently. Integration of processes and specialized tools that text mining can use (text mining tools), allow for data collection from many sources. It happens that employees have access to different versions of the same file, or important information is stored in many files or Excel sheets, on platforms, and elsewhere – thanks to text mining integrated with our sources, we get everything in one place, and we have control over what information reaches employees – whether it is outdated and whether everyone is working on the same data. Of course, the general use and analysis of an organization’s textual data and deriving important conclusions thanks to text mining

is a completely new quality – we will address this separately in the paragraph below.    

  Text mining as a data analysis tool – how to use it?Text mining allows for more efficient collection and use of current and future data that a company will possess. Sometimes we don’t even realize what important conclusions and insights are just waiting to be discovered – without a modern approach to automating analysis, many opportunities for process improvement can pass us by. If we want, for example, to conduct surveys to find out from our customers or employees how they think our products or processes are working, it will be easiest to automatically analyze the answers to yes/no questions or rating questions, e.g., from 0 to 10. Open-ended questions can be difficult, especially if there are a lot of responses and each respondent writes in their own style, in bullet points, in full sentences – differently. A human can, of course, read all these surveys and collect data regarding satisfaction or general comments, but they will do it in an incredibly long time, errors are not excluded, and this also entails significant costs. Can it be done better? Of course, text mining can help. Thanks to this technology, we are able to analyze this unstructured textual data in its entirety, quickly, and spot hidden patterns. This, in turn, will help us respond to the needs of employees, customers, and stakeholders; it will help optimize and improve process efficiency and direct employees to where they can really benefit from their “human” work and productivity. If we work in an international company or one that has customers from different countries around the world, translating some (or all) documents and information becomes essential. Feedback can also flow to us in different languages if we adapt, for example, our satisfaction survey questions to the language of the customer or employee. Text mining tools allow for the automatic translation of these original comments while preserving their original meaning, which has a great impact on the subsequent interpretation of the actual rating, taking into account linguistic nuances. Moving slightly away from the typical enterprise – text mining as a tool for rapid data analysis proves essential, for example, in clinics or hospitals, where we deal with decades of collected various tests, described patient cases, published articles, or descriptions of drugs and clinical trials – all of this can be useful if the organization is working on releasing a new drug, but also if support is needed in diagnosing a specific patient. Text mining allows for efficient and automatic analysis, thereby providing invaluable support to doctors and scientists in their work. Going in another direction, the legal or insurance industries also collect a large part of their data in text form. Text mining

can quickly find relevant records in contracts, incident descriptions, or court records and streamline activities such as claims reporting or risk analysis.    

  Text mining and data visualization – how to combine these fields?Data visualization, or data visualization, often refers to creating charts and numerical tables, but let’s not forget that text can also be presented in many ways, facilitating its analysis. After all, text is also data. Why specifically use text data visualization? For example, to summarize a large amount of text – we can automatically highlight and mark specific expressions, words, or sentences, create thematic categories, and categorize by type and sentiment of opinion (favorable, neutral, negative). Creating a word cloud, which is an automatic visualization of the most frequently used words in a text, or a special dashboard to display more visualizations, will not be a problem for text mining

technology tools in combination with AI and Machine Learning. Thanks to such visualization, we will more easily find hidden meanings, trends, and patterns in customer responses or other textual data. We will see charts with a percentage breakdown of the requested query – besides sentiment analysis, we can also see how many comments contained a question, an opinion, or a request for contact, and what exactly they concerned (if we have different products in our offer and are analyzing sales inquiries) – the possibilities are truly limitless. We just need to know what we want to see in our visualization.    

Text mining in e-commerce – how can it help with sales?   One of the very helpful ways that text mining can be used in e-commerce is by recommending products that may be in the area of interest of buyers (header: “You might also like… ” after adding something specific to the cart in an online store) – text filtering technology catches and suggests to the customer what else might be useful to them. Such offers will work great if they are supported by real data – random recommendations will be of no use. Another example of using text mining in e-commerce is data analysis, which allows for issuing personalized discounts and offers. A significant majority of buyers (as many as 89%, repeating after a Software Advice report) will choose a store that offers discounts tailored to their previous, individual purchases. We will also know exactly what potential buyers are looking for, which is why personalized offers will attract them and keep them as customers for longer. We mentioned earlier that text mining can help us fight cyberattacks – it is no different in the e-commerce industry, where fraudsters can wreak havoc by buying a product, using it, and returning it under false pretenses of poor quality, which in higher intensity can very negatively affect the store’s results. Hackers and cybercriminals using stolen credit cards or entire identities are also much easier to detect if we have text mining and Machine Learning technologies on our side, filtering suspicious data.    

Text mining in marketing – what opportunities does it provide in this area?

  You probably already have some idea of how much greater the possibilities for using textual data text mining gives us. As for marketing departments, they will certainly benefit greatly from this technology if it is implemented correctly. First of all, we will be able to identify types of opinions – sentiment analysis mentioned earlier is a great tool for marketers and allows for a quick understanding of our readers’ reactions on blogs or social media. By correctly interpreting this data, we can much more accurately select marketing campaigns and their topics based on what people want and what they need from us. Translating from different languages and categorizing responses from all kinds of surveys are also a great help for marketing. Automating these activities leads to significant savings in time and resources, and it will be much easier for us to notice trends in customer comments – not only regarding what works but also what is not necessarily right in our company or products. Thanks to this, we are able to react quickly and improve those aspects of our offer that customers expect to be improved or changed. Thanks to text mining we are able to change the quality of data analysis in the organization for the better. The possibilities are truly limitless – if you are not sure what you can use text mining for in your organization, please contact us.