Text Mining and Natural Language Processing

What is Text Mining?

Text mining definition - the process of obtaining high-quality information from text. It is also known in some circles as text data mining, which is somewhat similar to text analytics. It involves the use of computers to automatically extract data from various written sources to discover new information that was previously unknown.

It is widely used in knowledge-based organizations. Examine a large number of documents, often for research purposes. Text mining is a tool for identifying patterns, uncovering relationships, and making claims based on patterns buried deep in layers of textual big data. Once extracted, the information is transformed into a structured format that can be further analyzed or categorized into grouped HTML tables, mind maps, and diagrams for presentation. It can be integrated into data warehouses, databases or business intelligence dashboards for analysis.

Types of Analysis Performed on Data Extracted Through Text Mining

Data extracted through text mining is valuable for performing different types of analysis:

• Normative analysis

• Predictive analytics

• Descriptive analysis

• Lexical analysis - checking the frequency distribution of words

• Mark and Annotate

• Pattern recognition

• Links and Associations

• Visualization

Basically, the goal is to convert text into data for analysis using Natural Language Processing (NLP), various types of algorithms and analytical methods. Interpreting the information collected is an important part of this process.

The Capabilities of Today's Natural Language Processing Systems

Natural language understanding is the first step in natural language processing that helps machines read text or speech. In a way, it simulates the human ability to understand actual languages ​​such as English, French or Mandarin.

Natural language processing combines natural language understanding and natural language generation. This in turn simulates the human ability to create text in natural language. Examples include the ability to gather or summarize information, or participate in a conversation or dialogue.

Natural language processing has grown by leaps and bounds over the past decade and will continue to evolve and grow. Mainstream products like Alexa, Siri, and Google's voice search use natural language processing to understand and respond to user questions and requests.

Natural language processing systems are a form of automation that has become indispensable in today's text-derived data analysis. Your skills are varied:

1. You can consistently, tirelessly, and impartially analyze virtually unlimited amounts of text data.

2. You have the ability to understand complex and complex concepts.

3. You can identify linguistic ambiguities, extract relevant facts and identify connections.

4. You can provide a summary.

The Importance of Text Mining Today

Businesses around the world today are generating vast amounts of data by doing business online and doing business online almost every minute. This data comes from multiple sources and is stored in data warehouses and cloud platforms. Traditional methods and tools are sometimes insufficient to analyze such huge volumes of data, which are growing exponentially every minute, posing enormous challenges for companies.

Another major reason for adopting text mining is the increasing competition in the business world, which drives companies to look for higher value-added solutions to maintain a competitive edge.

This is the background in which data mining applications, tools and techniques have become popular. They provide a way to use all the data collected, which can then help organizations use it to grow.

How Does Text Mining Work Together With Natural Language Processing

An example of text mining relevance can be seen in the context of machine learning. Machine learning is a widely used artificial intelligence technique that enables systems to learn automatically from experience without programming. The technology can match or even surpass humans when it comes to solving complex problems with extreme accuracy.

However, for machine learning to achieve optimal results, it requires carefully curated inputs for training. This is difficult when most of the available data input is in the form of unstructured text. Examples of this are electronic patient records, clinical research datasets, or full-text scientific literature.

Natural language processing is an excellent tool for extracting structured and clean data for these advanced predictive models that machine learning uses as the basis for training. This reduces the need for manual annotation of such training data, and save costs.

Additionally, text mining enables analysis of large volumes of literature and data to identify potential problems early in the pipeline. This helps companies make the most of their R&D resources and avoid potential known errors in functions such as late-stage drug trials.

The Multidisciplinary Nature of Text Mining

Text mining is a multidisciplinary field in every respect. It contains and integrates data mining, information retrieval, machine learning, computational linguistics and even statistical tools. It deals with natural language text stored in semi-structured or unstructured formats.

Text Mining Process: Steps

Preprocessing operation

• Assemble unstructured text data from multiple data sources: plain text, Word files, PDF files, web pages, blogs, emails, or social media.

• Use text mining tools and applications to detect and remove anomalous or redundant data hygiene and cleaning. This part of the process includes extracting and retaining only relevant information from the data and helping to identify the roots of certain words.

• Convert the above into a structured format suitable for analysis.

Analyze

• Analyze patterns in data through a management information system (MIS).

• Extract valuable insights and transfer information into a secure database to drive trend analysis.

• Use insights to make decisions text mining technology.

The Text Mining Techniques

There are five commonly used and effective techniques in text mining.

Information extraction

This technique refers to the process of extracting meaningful information from large amounts of data, whether they are in unstructured or semi-structured text format. It focuses on identifying and extracting entities, their attributes and their relationships. The extracted information is stored in a database for future access and retrieval. Precision and recall methods are used to assess the relevance and validity of these outcomes.

Information recovery

Information retrieval techniques are more specific and refer to extracting relevant and associated patterns based on a specific set of words or phrases. Information retrieval systems use algorithms​​ to track and trace user behavior and collect relevant data. An example of this is the widely used search engine Google.

Classification

Classification is a form of supervised learning in which plain language text is classified into a set of predefined topics based on content. The system collects documents and analyzes them to find relevant topics or the correct index for each document.

The co-citation process is used as part of natural language processing to extract not only meaning from text records, but also actual synonyms and abbreviations. Currently, this process is an automated process with a wide range of applications, from personalized advertising to spam filtering. It is usually used when classifying web pages under hierarchical definitions. Its uses are many.

Clustering

As the name suggests, this text mining technique seeks to identify and locate intrinsic structures within a text database and organize them into subgroups (or, ‘clusters’) for further analysis. This is a vital and standard text mining technique.

The biggest challenge in the cluster-forming process is to create meaningful clusters from unclassified, unlabeled textual data with no prior lead information. Cluster analysis is used in data distribution. It also acts as a pre-processing step for other algorithms and techniques that can be applied downstream on detected clusters.

Summarization

Text summarization is the process of auto-generating a compressed version of a specific text, that contains information that may be useful to the end user. The goal of the summarization technique is to look through multiple sources of textual data to put together summaries of texts containing a sizable amount of information in a concise format. The overall meaning and intent of original documents are kept essentially unchanged. Text summarization integrates the various methods that use text categorization, such as decision trees, neural networks, swarm intelligence or regression models.

Text Mining Applications and Benefits

Today, text mining tools and techniques are used in a variety of industries and fields; academic, healthcare, organizations, social media platforms, and more.

1. Text mining for risk analysis, assessment and risk management

Organizations often bring new products and services to market without adequate risk analysis. Incorrect risk analysis can leave an organization behind on key information and trends that can help it miss out on growth opportunities or better connect with audiences.

Text mining techniques are the driving force behind risk management software that can be integrated into company operations. This text mining technique collates information from various textual data sources and makes connections between relevant insights.

The use of text mining technology allows enterprises to keep abreast of current market trends, obtain the right information at the right time, and discover potential risks in time. This means organizations can reduce risk and make agile business decisions.

2. Fraud detection using text mining and analytics

This application of text analysis and the mining tools within it remains a mainstay for insurance and financial companies. Such organizations collect most of their data in text format. Structuring this data and text-analyzing it using text mining tools and techniques helps such companies detect and prevent fraud. It can also help companies process warranty or insurance claims faster.

3. Text mining for excellence in business intelligence

Many companies across a variety of industries are increasingly using text mining techniques to gain superior business intelligence insights. Text mining techniques provide deep insights into customer/buyer behavior and market trends.

Text mining can also help companies conduct strengths, weaknesses, opportunities and threats analysis of their own companies as well as their competitors and gain market advantage.

Text mining tools and techniques can also provide insight into the performance of marketing strategies and campaigns, what customers are looking for, their buying preferences and trends, and changing markets.

4. Improve customer service with text mining techniques

Text mining techniques are increasingly used in customer support to improve the overall customer experience. Natural language processing is a pioneer in this field. Businesses invest in text analytics software that examines textual data from customer surveys, feedback forms, voice calls, emails, and chats.

The goal of text mining and analytics is to reduce response times to calls or inquiries and to be able to handle customer complaints faster and more efficiently. This has the benefit of extending customer lifespan, reducing customer churn and resolving complaints faster.

5. Social media analysis with text mining tools

Given the sheer volume of text in social media, text mining tools excel at analyzing your brand's posts, likes, comments, testimonials, and follower trends. In fact, there are several tools designed to analyze how your brand is performing on different social media platforms.

Social media text mining is also an invaluable tool for gaining real-time insight into the responses and behavioral patterns of the vast array of people who interact with your brand and online content.

This enables text mining and analytics to help businesses capitalize on current trends in reaching audiences and simplifies text mining, content aggregation, newsletter creation and content clustering.

InfoNgen is an AI-based text analytics software. It leverages the power of NLP and machine learning to search, collect and analyze text from more than 200,000 sources including public, internal and social media sites.

Disadvantages of Text Mining

While text mining or web mining techniques do not pose problems by themselves, applying them to private datasets can raise ethical issues. This includes using text mining on individual medical records or creating group profiles. Privacy concerns are a much-criticized ethical issue associated with the unethical use of text mining.

Additionally, companies may perform text mining for specific purposes but use the data for other undisclosed or undisclosed purposes. In a world where personal data is a commodity, such misuse poses a significant threat to individual privacy.

Nonetheless, text mining remains an extremely powerful tool that many companies can leverage, from streamlining day-to-day operations to making strategic business decisions.