What is text mining and how is it used?

Text mining is a sub-area of data mining that focuses on analyzing un­struc­tured or weakly struc­tured text data and complex data sets. Text mining software based on Natural Language Pro­cess­ing, deep learning and big data is used to open up and structure text data and identify important findings, struc­tures and cor­re­la­tions.

What is text mining?

Text mining, also known as text data mining, is a spe­cial­ized sub-area of data mining. The process involves ex­tract­ing and analyzing in­for­ma­tion from large databases, data sets and primarily weak and un­struc­tured texts. The data to be analyzed is developed using various analysis tech­niques and converted into a struc­tured form. This allows valuable insights, in­for­ma­tion and mean­ing­ful struc­tures and patterns to be iden­ti­fied.

Un­struc­tured formats such as documents, emails, posts on social media or forums, as well as the content of text databases are analyzed. As they can differ greatly in terms of semantics, syntax, ty­pog­ra­phy, size, subject matter and language, text mining offers the advantage of efficient pre-pro­cess­ing and analysis of large data sets for various purposes. These include sentiment analysis, applicant screening, market research, science and customer service.

How does text mining work?

Text mining is similar to data mining in the way it works but focuses on the analysis of un­struc­tured or weakly or partially struc­tured data. As around 80 percent of all data is available in un­struc­tured formats, text mining software fa­cil­i­tates the pro­cess­ing and prepa­ra­tion of documents and large data sets. For this purpose, text data is analyzed, converted into a struc­tured form, clustered and cat­e­go­rized using modern quan­ti­ta­tive and qual­i­ta­tive analysis tech­nolo­gies such as natural language pro­cess­ing and deep learning.

The text mining process can be broken down into the following steps:

1. Data prepa­ra­tion and text prepa­ra­tion: Texts are first collected from various sources and in different formats. These include, for example, emails, documents, website content or the­mat­i­cal­ly cat­e­go­rized databases. Once the data records have been collected, the texts are struc­tured, nor­mal­ized and cleaned up. Words are reduced to root and normal forms through stemming and lemma­ti­za­tion, different word variants are stan­dard­ized, unim­por­tant special char­ac­ters and stop words are removed or texts are broken down into in­di­vid­ual com­po­nents, also known as tokens, in order to use them for clus­ter­ing or document com­par­isons.
2. Text prepa­ra­tion: Keywords, phrases, patterns or common struc­tures are iden­ti­fied in the prepared data set. Further pro­cess­ing steps include marking and sum­ma­riz­ing data records, ex­tract­ing text prop­er­ties (e.g., frequent phrases and words), as well as cat­e­go­riz­ing and clus­ter­ing the data.
3. Analysis: After prepa­ra­tion and editing, various analysis models are used to reveal important insights and struc­tures from cat­e­go­rized, clustered, grouped or filtered data sets through keyword ex­trac­tion or pattern recog­ni­tion. Tech­niques such as hi­er­ar­chi­cal clus­ter­ing, topic modeling, sentiment analysis or text summaries are used to identify relevant entities, re­la­tion­ships and patterns.
4. In­ter­pre­ta­tion and modeling: Based on the findings of modern deep learning and analysis tech­nolo­gies, the knowledge gained is analyzed and trans­ferred into data models, business strate­gies and forecasts. By ex­tract­ing in­for­ma­tion and analyzing patterns and trends, op­ti­miza­tion potential for products and services can be iden­ti­fied or large volumes of data can be ef­fi­cient­ly evaluated and processed.

In what areas is text mining used?

Software for text mining and data mining is used in a wide range of in­dus­tries and ap­pli­ca­tion areas. It’s used for com­mer­cial as well as sci­en­tif­ic or security purposes. Common text mining ap­pli­ca­tions include:

• Customer service: Text mining optimizes the customer and user ex­pe­ri­ence by combining feedback functions such as chatbots, ratings, support tickets, surveys or social media data. This allows problems and potential for im­prove­ment to be quickly iden­ti­fied through sentiment analysis and user behavior, inquiries to be processed ef­fi­cient­ly and customer loyalty to be increased. Text mining software also relieves the burden on companies that are faced with a shortage of customer service staff.
• Sentiment analysis: By eval­u­at­ing and analyzing feedback, reviews or customer com­mu­ni­ca­tion, mood swings and the public per­cep­tion of brands, campaigns and companies can be specif­i­cal­ly analyzed. Based on this, products and services can be adapted and optimized.
• Risk man­age­ment: Text mining in risk man­age­ment monitors changes in sentiment and iden­ti­fies key fluc­tu­a­tions or areas of focus in reports, state­ments or white papers. For example, text mining can promote in­vest­ments by helping financial in­sti­tu­tions better un­der­stand trends and de­vel­op­ments in in­dus­tries or financial markets.
• Main­te­nance and servicing: Text mining extracts and iden­ti­fies important technical process data that’s important for optimum con­di­tions, machine per­for­mance and product quality. This allows patterns and trends or even weak­ness­es in main­te­nance processes to be iden­ti­fied, or the causes of mal­func­tions, break­downs or pro­duc­tion errors to be found.
• Health­care: In the medical field, text mining helps to search and cat­e­go­rize extensive or complex spe­cial­ist lit­er­a­ture. This allows valuable in­for­ma­tion on symptoms, diseases and treatment pro­ce­dures to be found quickly, cor­re­la­tions to be better iden­ti­fied, treatment times shortened, research costs reduced, treatment methods optimized, and valuable research findings cor­re­lat­ed.
• Spam filter: Text mining can play an important role in the detection and filtering of spam emails to reduce the risk of cy­ber­at­tacks and to recognize malware and spam based on patterns, struc­tures and phrases.
• Applicant screening: The struc­tured analysis of ap­pli­ca­tion documents makes it easier to select suitable can­di­dates with the key qual­i­fi­ca­tions you’re looking for.
• In­for­ma­tion retrieval: The search and ex­trac­tion of in­for­ma­tion and data can improve in­for­ma­tion retrieval, for example specif­i­cal­ly for search engines or search engine op­ti­miza­tion.

What are the ad­van­tages of text mining?

Text mining is a powerful and versatile tool for analyzing and unlocking un­struc­tured data and improving various business processes and functions. By providing important insights into data sets, text mining offers the following ad­van­tages, among others:

• Early detection of problems: Iden­ti­fies product and business issues early based on insights from customer feedback and com­mu­ni­ca­tions to optimize processes and services.
• Product and service im­prove­ment: Makes im­prove­ments to products or services requested by customers clear. The analysis of customer needs enables an improved quality of marketing and customer service through a per­son­al­ized and targeted approach and faster pro­cess­ing of inquiries.
• Pre­dic­tion of customer churn: Shows trends that indicate potential customer churn through user behavior or reviews. This allows measures to be taken to strength­en customer loyalty and sat­is­fac­tion.
• Fraud detection: Detects anomalies and con­spic­u­ous patterns in text data or documents that can ensure early pre­ven­tion of fraud or spam.
• Risk man­age­ment: Insight into business trends and risks based on reports, documents and media provides relevant knowledge that fa­cil­i­tates decision making in risk man­age­ment.
• Op­ti­miza­tion of online ad­ver­tis­ing: Optimized seg­men­ta­tion of target groups allows ad­ver­tis­ing campaigns to be improved, ad­ver­tis­ing measures to be con­trolled in a more targeted manner and leads or con­ver­sions to be generated.
• Medical diagnosis: By analyzing and eval­u­at­ing patient, ex­am­i­na­tion and treatment reports, symptoms can be clas­si­fied more quickly, diagnoses can be made faster and treatment times can be shortened.
• Improved data quality and ef­fi­cien­cy: Large and un­struc­tured data is better cleansed and struc­tured to remove redundant data and improve data quality and usability. Data records can thus be processed and cat­e­go­rized more ef­fi­cient­ly and quickly.

What’s the dif­fer­ence between text mining and data mining?

Although text mining and data mining are similar, and text mining is con­sid­ered part of data mining, there are clear dif­fer­ences. In contrast to data mining, text mining in par­tic­u­lar analyzes un­struc­tured or partially struc­tured text data such as emails, documents, social media posts or text databases. The software extracts in­for­ma­tion in order to identify patterns, keywords or trends and structure data sets. Data mining in turn primarily examines struc­tured data from databases or tables in order to extract in­for­ma­tion and identify patterns, trends and cor­re­la­tions.

Tech­nolo­gies such as deep learning and above all Natural Language Pro­cess­ing play an important role in text mining, while data mining relies on math­e­mat­i­cal and sta­tis­ti­cal analysis methods and al­go­rithms. Despite this dis­tinc­tion, it can be said that the tran­si­tions between data mining and text mining can be fluid depending on the analysis method, objective and data sets.

Which tech­nolo­gies are used in text mining?

Text mining is a branch of data mining that uses ap­proach­es such as ar­ti­fi­cial in­tel­li­gence, machine learning and various other data science tech­nolo­gies to analyze text data.

Natural Language Pro­cess­ing forms an important text mining foun­da­tion by enabling software to un­der­stand, infer and process human language. Machine learning in turn uses al­go­rithms to recognize patterns, make pre­dic­tions, train computers and optimize processes. Deep learning is a spe­cial­ized form of machine learning that uses neural networks to identify complex re­la­tion­ships in large amounts of text and increase the accuracy of analysis.

Other tech­niques include language iden­ti­fi­ca­tion to determine the language of the text and to­k­eniza­tion, which breaks down texts into segments such as words or phrases. Part-of-speech tagging assigns a gram­mat­i­cal role to each word, while chunking groups neigh­bor­ing words into mean­ing­ful units. Syntax analysis (parsing) analyzes gram­mat­i­cal sentence structure to identify re­la­tion­ships between words and capture text meanings. These tech­nolo­gies enable in-depth analysis and use of text data in­di­vid­u­al­ly or in com­bi­na­tion.