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.