{"id":168150,"date":"2023-05-12T17:23:17","date_gmt":"2023-05-12T16:23:17","guid":{"rendered":"https:\/\/liora.io\/en\/?p=168150"},"modified":"2026-02-06T09:02:55","modified_gmt":"2026-02-06T08:02:55","slug":"text-mining-all-you-need-to-know","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/text-mining-all-you-need-to-know","title":{"rendered":"Text mining: Definition, techniques, use cases"},"content":{"rendered":"<style><br \/>\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style>\n<p><strong>Text mining consists in using Machine Learning for text analysis. Discover all you need to know: definition, functioning, techniques, advantages, use cases&#8230;\nModern companies have a lot of data on their customers or their business sector. New digital technologies such as social networks, e-commerce, or mobile applications for smartphones give access to a vast amount of information.<\/strong><\/p>\nBy analyzing this data, it is possible to discover untapped opportunities or alarming problems that need to be addressed urgently. However, some types of data are more difficult to exploit than others.\n\nData from social networks or other websites are mainly texts: comments on posts, product reviews, and complaints on community forums&#8230;\n\nHowever, texts are part of the so-called &#8220;unstructured&#8221; data. This information cannot be properly processed by traditional data analysis software and tools. It is, therefore, necessary to rely on &#8220;Text Mining&#8221;.\n\nText mining, or text analysis, consists of transforming unstructured text into structured data and then proceeding with the analysis. This practice is based on the technology of &#8220;Natural Language Processing&#8221;, which allows machines to understand and process human language automatically.\n\nArtificial intelligence is now able to automatically classify texts by sentiment, subject, or intent. For example, a text mining algorithm can review product reviews to determine whether they are mostly positive, neutral, or negative. It is also possible to identify the most frequently used keywords.\n\nIn this way, companies can analyze large and complex data sets in a simple, fast, and efficient way. This discipline also reduces time wasted on manual and repetitive tasks.\n\nTeams save time and can focus on more important tasks that require human intervention. And business leaders can leverage data to make better decisions.\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/machine-learning-engineer\">Discover the Machine Learning Engineer track<\/a><\/div><\/div>\n\n<iframe title=\"What is Text Mining?\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/I3cjbB38Z4A?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<h3>How does Text Mining work?<\/h3>\nText mining is based on Machine Learning, a subcategory of artificial intelligence, which encompasses many techniques and tools that enable computers to learn to perform tasks autonomously.\n\nMachine Learning models are trained on data to be able to make accurate predictions. Text mining is the automation of text analysis using Machine Learning. To achieve this, the algorithms are trained using text as example data.\n\nThe first step is to assemble data. This data can come from internal sources, such as chat interactions, emails, surveys, or company databases. It can also come from external sources such as social networks, review sites, or news articles.\n\nThe data must then be prepared using various Natural Language Processing techniques. This &#8220;data pre-processing&#8221; aims to clean and transform the data into a usable format.\n\nThis is an essential aspect of Natural Language Processing, involving the use of different techniques such as language identification, tokenization, part-of-speech labeling, chunking, and syntax analysis. The objective of these different methods is to format the data for analysis.\n\nAfter completing this &#8220;pre-processing&#8221; of the text, it is time for data analysis. Various text-mining algorithms are used to extract information from the data.\n<h3>Text mining methods and techniques<\/h3>\nThere is a wide variety of text mining techniques and methods. Here are the most commonly used.\n<h5>Analysis techniques<\/h5>\nThe &#8220;word frequency&#8221; technique consists of identifying the most recurrent terms or concepts in a data set. This can be very useful, especially when analyzing customer reviews or conversations on social networks.\n\nFor example, if terms such as &#8220;too expensive&#8221; or &#8220;overpriced&#8221; recur frequently, the analysis may suggest that the product is too expensive. It is, therefore, necessary to adjust the price if possible.\n\nThe collocation method, on the other hand, consists of identifying sequences of words that frequently appear close to each other. Some words appear together very often. These may be bigrams or trigrams, combinations of two or three words. By identifying these colocations, it is possible to better understand the semantic structure of a text and to obtain more reliable Text Mining results.\n<h5>Information retrieval<\/h5>\nInformation retrieval is the process of finding relevant information from a pre-defined set of queries or phrases. This approach is often used in library catalog systems or web search engines.\n\nIR (information retrieval) systems use different algorithms to track user behavior and identify relevant data. Tokenization&#8221; consists of breaking down a long text into sentences or words called &#8220;tokens&#8221;. These tokens are then used in models for text clustering or document association tasks.\n\nStemming, on the other hand, consists of separating the prefixes and suffixes of words to derive the root word and its meaning. This technique reduces the size of index files.\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Know more about our courses<\/a><\/div><\/div>\n\n<h5>Text classification<\/h5>\nThere are also more advanced methods of text mining. Text classification consists of assigning labels to unstructured text data. This is an essential and indispensable step for Natural Language Processing.\n\nIt allows to organization and structure of a complex text to extract relevant data. It is thanks to this technique that companies can analyze all kinds of textual information to extract valuable information.\n\nThere are different forms of text classification. Topic Analysis is used to understand the main themes or topics of a text. This is one of the main ways to organize text data.\n\nSentiment Analysis is the analysis of the emotions in a text. This allows for a better understanding of customer opinions, for example, by reviewing comments about a product. Text can be classified as positive, negative, or neutral.\n\nLanguage detection consists of classifying a text according to its language. For example, it will be possible to sort customer service requests and redirect them to an advisor or agent who masters the appropriate language. This saves precious time.\n\nFinally, intention detection allows for the automatic recognition of the intentions of a text. For example, the analysis of different responses to an advertising email can determine which interlocutors are interested in a product.\n<h5>Information extraction<\/h5>\nAnother text mining technique is text extraction. It aims at extracting specific data from a text, such as keywords, proper names, addresses, or emails. This avoids having to sort the data manually and therefore saves time.\n\nOne can select the features that contribute most to the results of a predictive analysis model, extract features to improve the accuracy of a classification task or detect and categorize specific entities in a text.\n\nIt is of course possible to combine text mining and text classification, or other text mining methods in the same analysis.\n\n<iframe title=\"Text Mining Techniques\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/E0p5p90onDA?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<h3>Text Mining vs. Text Analytics: what is the difference?<\/h3>\nText mining is often confused with text analytics. In reality, they are two slightly different concepts.\n\nBoth aim at automatically analyzing texts but are based on different techniques. Text mining identifies relevant information in text, while text analytics aims to discover patterns across large datasets.\n\nOne provides qualitative analysis and the other quantitative analysis. In general, Text Analytics is used to create tables, charts, graphs, or other visual reports.\n\nText mining combines statistics, linguistics, and machine learning to automatically predict outcomes from past experiences. Text Analytics, on the other hand, is about creating data visualizations from the results of Text Mining analyses. It is of course possible to combine these two approaches.\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Discover our Data Scientist track<\/a><\/div><\/div>\n\n<h3>The Benefits of Text Mining<\/h3>\nText mining has many advantages, at a time when companies and individuals generate huge volumes of data every day. Indeed, nearly 80% of text data is unstructured. It is therefore impossible to analyze it without using text mining.\n\nFor example, emails, social media posts, messenger discussions, customer service requests, surveys&#8230; it is very difficult to sort out this information manually.\n\nText analytics allows you to analyze large volumes of data in just a few seconds, thus increasing productivity. These analyses can be performed in real-time, and it is, therefore, possible to intervene immediately if a problem is detected.\n<h3>How can Text Mining be used?<\/h3>\nText mining can be used in many ways by companies. The applications of this technology are limitless and extend to all industries.\n\nIt can be used to automate text analysis for marketing, product development, sales, and customer service. Teams can become more efficient and productive by focusing on more important tasks.\n<h5>Customer service<\/h5>\nIn the field of customer service, it is for example possible to automatically sort requests. Text mining automatically identifies the topics, intent, complexity, and language of the requests to organize them. This allows agents to focus on helping customers.\n\nIf a request is more important or urgent than another, it can be automatically prioritized and processed before others. In addition, text analytics can also be used to measure customer service efficiency and user satisfaction.\n\nText mining is also very useful for analyzing customer feedback and opinions about the brand and its products. This allows you to understand their opinions, but also their expectations and the quality of their experience with your company.\n\nProduct reviews, comments on social networks, and survey responses can be scrutinized. In this way, it is possible to use the data to make the right decisions and improve weak points.\n<h5>Risk management<\/h5>\nText mining is used in the field of risk management. It can be used to extract information about industry trends or financial markets by monitoring changes in sentiment or extracting information from analytical reports and white papers.\n\nThis can be very useful within banking institutions. This is because the data allows them to approach investments in different sectors with more confidence. Many banks are now taking this approach.\n<h5>Maintenance<\/h5>\nText mining offers a complete overview of the activity and operation of industrial equipment and machinery. It allows for to automate maintenance decisions.\n\nFor example, it is possible to highlight patterns and trends suggesting the occurrence of a problem. In this way, it is possible to implement predictive maintenance measures to intervene before it is too late. Maintenance operations can then be carried out proactively.\n<h5>Healthcare<\/h5>\nIn the field of health, Text Mining techniques are increasingly used by researchers. For example, information clustering allows to extract information from medical books in an automated way.\n\nThis saves time and money. Thus, this approach is proving to be of great help to the world of medicine and health.\n<h5>Cybersecurity<\/h5>\nText analysis can also be particularly useful for cybersecurity. For example, it is possible to detect and filter spam automatically in email boxes.\n\nThis way, hackers can no longer use the spam method to hack into computer systems. The risk of cyber attacks is drastically reduced, and the user experience is also improved.\n\n<iframe title=\"Industry Applications of Text Analytics\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/SRBVRe_RMcM?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<h3>How to get trained in Text Mining?<\/h3>\nText data is becoming more and more numerous, and text analysis is becoming essential for data-driven companies in all sectors. To learn how to master Text Mining and its subtleties, you can turn to Liora training courses.\n\nThis discipline is part of our Data Analyst and Data Scientist courses. These two courses will train you respectively as an analyst and as a data scientist, for which Text Mining plays a central role.\n\nAll our courses are distinguished by an innovative &#8220;Blended Learning&#8221; approach, combining classroom and distance learning. You will benefit from the flexibility of online training while remaining motivated thanks to the face-to-face masterclasses.\n\nThese courses can be completed in just a few weeks in the intensive BootCamp format, or in a few months in Continuing Education, which can be combined with a personal or professional activity.\n\nAt the end of these programs, learners receive a diploma certified by the Sorbonne University. 90% of learners find a job at the end of the program. Don&#8217;t wait any longer and discover our courses.\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Start a Data Science course<\/a><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Text mining consists in using Machine Learning for text analysis. Discover all you need to know: definition, functioning, techniques, advantages, use cases&#8230; Modern companies have a lot of data on their customers or their business sector. New digital technologies such as social networks, e-commerce, or mobile applications for smartphones give access to a vast amount [&hellip;]<\/p>\n","protected":false},"author":74,"featured_media":168151,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2433],"class_list":["post-168150","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/168150","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/74"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=168150"}],"version-history":[{"count":1,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/168150\/revisions"}],"predecessor-version":[{"id":206409,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/168150\/revisions\/206409"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/168151"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=168150"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=168150"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}