What is a Text Mining in Data Mining – A Complete Guide

Text Mining, also referred to as text data mining, is the procedure of modifying text that is not structured into structured form in order to recognize significant patterns and the latest insights.

By using advanced systematic techniques such as Support Vector Machines (SVM) and Naive Bayes, businesses can uncover hidden relationships in their unstructured data using deep learning algorithms.

Moreover, text mining is extensively used in knowledge-driven companies. Text mining distinguishes facts, relationships, and declarations because if not, then they would be left concealed in the textual big data.

When this information is extracted, it is transformed into a structured form that can be analyzed or presented straight away using grouped HTML tables, charts, mind maps, etc. Text mining utilizes diverse methodologies for processing the text, and one of the most essential methodologies of this is Natural Language Processing (NLP).

Understanding Text Mining

The structured data produced by text mining can be integrated into databases, data warehouses, or business intelligence dashboards and is used for descriptive, prescriptive, or predictive analytics. Text is the most ordinary data category within databases.

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The data needs to be organized according to the database, we can organize it as:

1. Structured Data: The data is systematized into the form of tables that contain innumerable rows and columns. This makes it simpler to stock and the analyze process along with machine learning algorithms. The data that is organized can include inputs like titles, numbers, as well as addresses.
2. Unstructured Data: Unstructured data is not in a form that is prearranged. It can contain text from different sources, such as reviews of products or from social media. It can also include formats of rich media, such as audio along video documents.
3. Semi-Structured Data: It is understandable by the name. This data is a mixture of structured as well as unstructured forms of data. Although it might have some arrangements, it does not have the sufficient structure necessary to fulfill the relational database’s requirements. Some semi-structured data examples involve files like HTML, XML, and JSON.

As in the world, most of the data is in unstructured form, therefore, for various business organizations, text mining can be a beneficial practice.

Text mining has several tools and techniques of Natural Language Processing (NLP). One such technique is information extraction which converts unstructured documents into a structured format for better analysis and quality insights. This enhances the organization’s decision-making process, which leads to improved business results.

Also Read =>> Comprehensive study of Structured and Unstructured data

Difference Between Text Mining and Text Analytics

Text mining and text analytics often can be confused as the same in meaning, but their meaning varies from each other. Text mining and text analysis both recognize the trends as well as the patterns of text residing in the unstructured data with the help of statistics, linguistics, and machine learning.

Text mining converts the data into an organized format, while with text analytics, more quantitative insights are discovered. Then the findings are communicated to vast audiences through Data Visualization techniques.

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Below mentioned are some differences between text mining and text analytics:

• Text mining and text analytics are utilized to solve the same problems, but both use different techniques and are combined in ways to derive meaning from the text automatically.
• Text analytics originated from the computational linguistics field. It has the potential to decipher human understanding into a sequence of linguistic rules that are generated by humans and are highly accurate. However, they aren’t able to adapt automatically and are frail in new situations.
• Text mining is the latest discipline that arose from the fields of statistics, data mining, and machine learning. It can form logical models from collections of historical data. Statistical models learn from training data and can adapt while identifying unknowns, resulting in improved memory. Nonetheless, they are susceptible to missing something that would be obvious to a human being.

Suggested Read => Qualitative vs Quantitative Data Analysis and Research

Uses of Text Mining

Text Mining is applied in several fields. The numerous applications of text mining are:

#1) Customer Service: Innumerable text mining techniques and tools are utilized to acquire trends as well as patterns from journals and proceedings that are gathered in text database repositories. These information resources are extremely helpful in the domain of research areas.

Digital libraries are valuable sources of textual data. It presents an innovative approach to acquiring productive data, allowing the access of virtual records in millions.

An international digital library that carries many languages and multilingual interfaces to provide a flexible method for deriving reports that operate in various formats, i.e. Microsoft Word, PDF, PostScript, scripting languages, HTML, and email.

Text mining processes execute distinct tasks such as collecting documents, determination, and enhancement, data removal, controlling substances, and creating summarization. There are various types of digital library text mining tools that are: GATE, Net Owl, and Aylien are utilized for text mining.

#2) Risk Management: Text mining is widely used in risk management, where it gives insights surrounding industry trends and financial markets by tracking shifts in sentiment and drawing out information from analyst reports and whitepapers.

This is certainly useful for banking institutions as this data gives more belief when business investments are considered over numerous sectors.

#3) Life Science: Life science and healthcare industries are generating a huge amount of textual as well as mathematical data which is based on patient records, medicines, sicknesses, treatments of diseases, and symptoms, etc. When it comes to forming decisions from a biological data repository, a major problem is filtering data and meaningful text.

The clinical records hold varying data that is unpredictable and extended. Text mining can assist in directing such types of data. Text mining is employed in the pharmacy industry, a clinical trade analysis examination, biomarkers disclosure, a clinical trade analysis examination, patent competitive intelligence, and clinical study as well.

#4) Maintenance: Text mining presents a complete picture of the functioning and usefulness of products and machinery. As time passes, decision-making is automated by text mining through revealing patterns that correspond with problems and preventive and reactive maintenance processes.

Maintenance professionals can quickly identify the main cause of challenges and failures with the help of text analytics.

#5) Healthcare: Text mining approaches are extremely beneficial to researchers in the biomedical field, specifically for grouping information. Manual examination of medical research can be highly expensive as well as time-consuming. Text mining contributes an automation method to derive productive information from medical literature.

Techniques of Text Mining

The procedure of text mining incorporates innumerable activities that allow it to gather information from unstructured text data. Initially, you need to begin with text processing before implementing text mining techniques. Text processing is the process of clearing as well as modifying the text data into a functional format.

In Natural Language Processing (NLP), text processing is a central feature. It generally includes using techniques like tokenization, language identification, chunking, syntax parsing, and part-of-speech tagging, to appropriately format the data in order to analyze. When the procedure of text processing is complete, algorithms of text mining can extract data insights.

Some of the common text-mining techniques include:

#1) Information Retrieval

Information Retrieval (IR) based on a prearranged collection of doubts and expressions provides meaningful knowledge or files. These systems use algorithms for tracking user behavior and for discovering significant data. The most common use of Information Retrieval is in library catalog systems and in well-known search engines, such as Google.

A few ordinary sub-tasks of IR involve:

• Tokenization: It is the practice of splitting long text into sentences and words that are called “tokens”. Afterward, these are used in models, to group text, and for matching documents.
• Stemming: It is the procedure of dividing the prefixes and suffixes from words in order to obtain the form of the root word and its meaning. Information retrieval is made easier through this technique as it reduces the indexing document size.

#2) Natural Language Processing

Natural Language Processing (NLP) is a technique of Artificial Intelligence (AI) that allows machines to process as well as understand languages similar to human beings.

It’s developed from computational linguistics and employs methods from distinct disciplines, like artificial intelligence, computer science, data science, and linguistics that help computers comprehend human language in written as well as verbal forms.

The sub-tasks of NLP enable computers to read by analyzing sentence structure and grammar. Some sub-tasks are:

• Summarization: This technique offers a summary of lengthy pieces of text in order to produce a clear, logical summary of a document’s main points.
• Part-of-Speech (PoS) tagging: This technique, in the document, allows a tag to each token based on its section of the speech, i.e. representing verbs, nouns, adjectives, etc. This step allows semantic analysis of the unstructured text.
• Text Categorization: This task, also known as text classification, is accountable for analyzing text documents as well as arranging them based on predefined topics or categories. This sub-task certainly comes in handy when synonyms and abbreviations are categorized.
• Sentiment Analysis: This technique identifies positive and negative sentiments from internal and external data sources, which allows one to keep an eye on variations in customers’ attitudes. This analysis is normally utilized to supply information regarding insights into brands, products, and services. These perceptions can procure a better connection between businesses and customers, which results in enhancing the processes and user experiences.

#3) Information Extraction

When searching through documents, Information Extraction (IE) identifies and reveals significant data segments. It centers on deriving organized information from the loose text as well and stores these entities, attributes, and related information in a database.

Information extraction sub-tasks usually include:

• Feature selection involves choosing the most important features to contribute to a predictive analytics model’s output.
• Feature extraction is the procedure of picking a subset of characteristics to upgrade a classification task’s precision. This is extremely essential for dimensionality depletion.
• Named-Entity Recognition (NER) is known as entity identification or entity extraction. Its objective is to discover and categorize particular entities in text, like names or locations. For instance, NER recognizes “Steve” as a man’s name and “California” as a location.

Importance of Text Mining

Every day, individuals and companies create huge quantities of data. Stats state that nearly 80% of the existing text data is unstructured. This indicates that it’s not organized, not searchable, and it’s quite impossible to handle and it is not useful at all.

Organizing, categorizing, and extracting relevant information from raw data is a massive worry for companies. Therefore, text mining is immensely significant for this.

In the business context, unorganized text data can be emails, social media posts, chats, support tickets, surveys, etc. When this type of information is classified manually, it results in failure more often than not. When done manually it is not only time-consuming and high-priced, but it’s inaccurate and impossible to scale as well.

Consequently, text mining has proven to be a trustworthy an economical way to attain precision, scalability, and swift response time.

Below are mentioned some of its top advantages:

1. Scalability: Text mining has made it possible to examine huge amounts of data in a few seconds. Companies can conserve a lot of time that can be utilized to center on other tasks. This results in an increase in the productivity of businesses.
2. Real-time analysis: Due to text mining, companies are able to prioritize pressing matters appropriately which incorporates, identifying a possible catastrophe and discovering product defects or negative reviews in real time. As it permits companies to take swift measures.
3. Consistent criteria: When working on iterative, manual tasks, they are bound to make more mistakes. It is difficult for them to maintain consistency as well as inspect data subjectively. For instance, in tagging, including categories in emails or support tickets, is a pre-occupying task that frequently leads to mistakes and irregularities. By automating this activity, plenty of time can be saved and it enables precise results as well as certifies that consistent criteria are implemented on every ticket.

Working of Text Mining

Text mining assists in analyzing huge volumes of raw data in order to discover meaningful insights. When it is integrated with machine learning, it can generate text analysis models that classify or extract certain information that is based on former training. However, text mining can sound complex, it is actually rather easy, to begin with.

The first step of text mining is to collect data. Let’s suppose you want to examine conversations with users via the Intercom live chat of your company. First of all, you are required to create a document holding this data.

Data can either be internal such as, interactions through chats, emails, surveys, spreadsheets, databases, etc. or it can be external, which is information from social media, review sites, news outlets, and any other websites.

The next step is to prepare your data. Text mining systems utilize numerous NLP techniques, such as tokenization, parsing, lemmatization, stemming, and terminate removal to manufacture the inputs of machine learning models. Afterward, is the text analysis itself.

Below we will discuss the two most common techniques of text mining and how they work. The techniques are as follows:

#1) Text Classification: Text classification is the procedure of allotting tags or categories to texts that are based on their content.

Fortunately, with the help of automated text classification, it has been made possible to tag a huge set of text data and acquire desirable results in a significantly brief period of time, and without any manual aid. Text classification has fascinating applications in distinct fields.

Rule-based Systems: These sorts of text classification systems are based on linguistic rules. By rules, we denote human-crafted associations between a certain linguistic pattern and a tag.

The algorithm is able to automatically identify the distinct linguistic structures and allot the correlating tags when it is coded with those rules. Generally, rules include references to syntactic morphology along with lexical patterns. They can be associated with semantic or phonological features.

Rule-based systems are simple to acknowledge since they are initiated as well as advanced by humans. Nevertheless, when it comes to adding the latest rules to an algorithm more often than not, it needs plenty of tests in order to see if they will have an impact on the predictions of other rules, which makes it hard to scale the system.

Moreover, for generating complex systems, specific knowledge is necessary of linguistics and of the data need analysis.

Machine Learning-based Systems: Text classification systems that are based on machine learning are able to grasps from the prior data, like examples. To make this possible, these systems require meaningful examples of text that are accurately categorized, the text input is known as the training data.

The training samples provided to the model need to be consistent as well as illustrative, in order to make precise predictions.

Machines are required to convert the training data into something they can comprehend, which in this case are vectors. Vectors are the collection of numbers along with encoded data. Vectors portray various aspects of the already existing data.

A most known approach for vectorization is known as a bag of words that counts the number of times a word, from a predefined collection of words, appears in the text you want to analyze. The text data converted into vectors, along with the anticipated predictions like tags, is fed to the machine learning algorithm.

The machine learning algorithms used in this are:

Naive Bayes: The Naive Bayes Theorem and the probability theory are used to predict the tag of a text. Vectors encode information that is established on the probability of words in a text that belongs to any of the tags in the model. This method can offer precise results whenever there is not enough training data.

Support Vector Machine (SVM): With the help of this algorithm, the vectors of tagged data are categorized into two distinct groups. The first group consists of most of the vectors that belong to a specified tag. Whereas, in the second group are the vectors that do not belong to that tag.

Usually, the results of this algorithm are much better than the results of Naive Bayes. However, this algorithm requires more coding strength for training the model.

Deep Learning Algorithms: Deep learning algorithms are similar to the way the human brain thinks. With the help of many training examples, they create extremely thorough depictions of data and are able to generate highly precise machine learning-based systems.

Rabin-Karp Algorithm: The Rabin-Karp string matching algorithm estimates a hash value for the arrangement and for every M-character substring of text that is about to be evaluated. When the values of hash are uneven, the algorithm will examine the pattern as well as the M-character sequence.

Through this, there is solely one assessment per text subsequence, and for the hash values to match, character matching is needed.

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#2) Text Extraction

Text extraction is the procedure of acquiring certain information from unorganized data. There are quantities of applications of text extraction in business. Such as, it is used to extract company names from a LinkedIn dataset, or it is used for recognizing the distinct aspects of product descriptions.

For instance, you have numerous agreements to inspect, with the help of text extraction the scanning of this data will be effortless and a text extractor can be used to obtain relevant information. All this can be done without actually reading the data. The execution of text extraction can be carried out by using different procedures.

The most common as well as trustworthy approaches are as follows:

Regular Expressions: Regular expressions explain a character’s sequence which can be linked with a tag. Every pattern is equivalent to rules in the rule-based approach for text classification. Each time the text extractor identifies a pattern that matches, it allows a corresponding tag.

The correct rules are formed in order to recognize as well as acquire the type of information. Then it’ll be easy to generate text extractors that provide high-quality results. Nevertheless, this procedure can be difficult to scale, mainly when patterns get more difficult and need various regular expressions to decide action.

Conditional Random Fields (CRF): Conditional random fields are a statistical technique that can be utilized for text extraction accompanied by machine learning. It forms a system that grasps patterns they require to obtain by considering distinct aspects from a sequence of words in a text.

CRFs have the ability to encode more information than regular expressions, which allows us to build more complex as well as stronger patterns. They are also able to form concepts based on what they have read. One drawback of this is that in order to train the text extractor effectively, detailed NLP knowledge and additional computing ability are required.

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Frequently Asked Questions

Q #1) What is text mining with examples?

Answer: Text mining is collecting data from numerous accessible sources in distinct document organizations. For instance, ordinary text, web pages, PDF files, etc. The data is pre-processed and cleared in order to differentiate as well as remove any discrepancy found in the data.

An example of text mining is, inadequate analysis is the foremost reason for failure. This is in particular true for financial as well as insurance industries. In such industries implementation of text mining technology in risk management can severely improve the potential to alleviate the risk.

Q #2) How does text mining work?

Answer: Natural language processing and artificial intelligence is used by text mining in order to reveal designs as well as relationships in unorganized text. Through NLP, the unstructured text is processed.

This pre-processing includes the following steps:

• Cleaning
• Stemming
• Tokenization
• Tagging parts of speech
• Parsing syntax

Afterwards, the processed data is sent to machine learning models that recognizes the patterns and relationships of the data documents. The machine learning models firstly require to be fed with documents that were tagged manually as fitting for a particular category or having a certain response.

The machine learning system from the training inputs forms an analytical model. The predictive models are fed with new documents, that appoint the suitable categorization or classifies the response of the file.

Q #3) Is text mining the same as NLP?

Answer: No, text mining and natural language processing are two separate techniques. Text mining is the technology of artificial intelligence, whereas natural language processing is a field of artificial intelligence. Natural language processing is utilized in text mining to get the desired results.

Q #4) What are the benefits of text mining?

Answer: Text mining has proved to increase efficiency by revealing concealed information and helps in creating new knowledge. It has also enhanced the research quality while providing evidence for it as well. It has benefited both the society as well as the economy.

Q #5) What are text mining tools?

Answer: Some text mining tools are as follows:

• MonkeyLearn
• Aylien
• IBM Watson
• Thematic
• Google Cloud NLP
• Amazon Comprehend
• MeaningCloud

Q #6) What is the difference between text mining and data mining?

Answer: Data mining deals with structured data that is data which is formatted like in databases. Whereas, text mining handles unorganized textual data, which is data that is not arranged previously like the data in social media, videos, etc.

Conclusion

In this article, we have thoroughly explained text mining, which is the process of converting unstructured data into a structured form in order to obtain relevant patterns, along with the types of data that can be organized. As people sometimes confuse text mining and text analytics as the same, we defined the difference between the two.

Next, we introduced the various techniques of text mining, which are Information Retrieval (IR), Natural Language Processing (NLP), and Information Extraction (EX). Later on, we considered the various applications of text mining in different fields.

We discussed its importance and how it has helped numerous industries. In the end, we meticulously covered the workings of text mining.