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Assalamualaikum my steemit family. Today I am going to share a very informative topic with you that is Text Mining.

Let's explore what is Text Mining?

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Text mining is the process of evaluating and analyzing large amounts of informal text data with the help of software that can identify concepts, patterns, topics, keywords and other attributes in the data.
Text mining has become very useful for data scientists and other users due to the development of large data platforms and in-depth reading algorithms that can analyze large sets of informal data sets.
Mining and analysis document enables organizations to obtain business information that may be relevant to corporate documents, customer emails, call center logs, meaningful survey comments, social media posts, medical records and other documented data sources.

How the text mine works?

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Document mining is naturally similar to data mining, but with a focus on text instead of more structured data types. However, one of the first steps in the mining process is to organize and organize data in a certain way so that it can be included in both quality and quantitative analysis.
Doing so often involves the use of natural language processing technology (NLP), which uses the principles of computational linguistics to analyze and interpret data sets.
Analysis models are then used to identify findings that can help drive business strategies and operations.

Techniques and Strategies

There are various methods and techniques for extracting text. In this section, we will cover some of the most common ones.

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•The frequency of the words

The word frequency can be used to identify frequent words or concepts in the data set. Finding the most frequently quoted words in informal text can be especially helpful when analyzing customer reviews, social media discussions or customer feedback.

•Colloction

Collocation means the sequence of words that often appear next to each other. The most common types of combinations of bigrams (two words that may be related, such as starting, saving time or making decisions) and trigrams (a combination of three words, such as during a distance or communication).

•Concordance

A concordance is used to identify a specific context or event from which a word or group of words appears. We all know that human language can be confusing: the same word can be used in many different situations. Analyzing a word concordance can help you understand its literal meaning based on the context.

•Text Separation

Text segregation is the process of assigning categories (tags) to random text data. This important function of Natural Language Processing (NLP) makes it easy to edit and edit complex text, turning it into logical data.
Thanks to text editing, businesses can analyze all kinds of information, from emails to supporting tickets, and get important information in a fast and economical way.
Below, we will refer to some of the more widely used textual distinctions - topic analysis, emotional analysis, language acquisition, and objective discovery.

•Topic analysis:

It helps you to understand the main themes or topics of the text, and is one of the main ways to organize text data. For example, a support ticket that says my online order has not yet arrived, can be classified as Shipping issues.

•Emotional Analysis:

It contains an analysis of the emotions that underpin any given text. Emotional analysis helps you to understand ideas and feelings in a text, and classify them as positive, negative or neutral.

•Language acquisition:

Allows you to parse text based on its own language. One of its most useful programs is to automatically move support tickets to the appropriate localized group. Doing this task automatically is easy and helps teams save valuable time.

•Objective :

You can use the text separator to see the intent or purpose of the text automatically. This can be especially helpful when analyzing customer conversations.

Data mining areas:

These are the following archeological sites:

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a)Extracting Information:

Automatic extraction of structured data such as businesses, business relationships, and attributes that describe businesses from an informal source is called information extraction.

b) NLP:

NLP represents the processing of Natural Language. Computer software can understand human language in the same way that it is spoken. NLP is actually a part of artificial intelligence (AI). Developing an NLP application is difficult because computers often expect people to "talk" to them in an accurate, clear, and structured programming language. Human speech is often untrue in order to rely on a number of complex variables, including slang, social status, and vernacular languages.

c)Data Mine:

Data mining means to the extraction of useful data, hidden patterns from large data sets. Data mining tools can predict future behaviors and trends that allow businesses to make better data-driven decisions. Data mining tools can be used to solve many business problems that are traditionally time consuming.

d)Information retrieval:

Data recovery is associated with retrieving useful data from data stored on our systems. Alternatively, as an analogy, we may view search engines that occur on websites such as e-commerce sites and any other sites as part of information retrieval.

Text Mining Process:

The text mining process includes the following steps to extract data from a document.

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Text conversion

Text conversion is a method used to control large letters of text.
Here are two major ways to represent the document.
Word bag
Vector space

Pre-Text Processing

Pre-processing is an important and important step in Text Mining, Indigenous Language Processing (NLP), and retrieval (IR) retrieval. In the field of text mining, pre-data processing is used to extract useful information and information from informal text data. Data Recovery (IR) is a matter of choosing which documents in the collection should be downloaded to meet a user's need.

Feature selection:

It is a very important part of data mining. Feature selection can be defined as the process of minimizing processing inputs or obtaining important sources of information. .

Data Mine:

Now, in this step, the process of digging the text meets the normal process. Classic Data Mining processes are used on a website.
Rate:
Next, it evaluates the results. Once the result has been checked, the result is discarded.

Text Mining Applications:

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The following are the text mining status:

•Risk Management:

Risk Management is a systematic and logical process of analyzing, diagnosing, treating and monitoring risks involved in any action or process in organizations. Insufficient risk analysis is often the leading cause of frustration. This is especially true of financial institutions where adoption of Risk Management Software based on textual mining technology can effectively enhance risk reduction capabilities. It enables control of millions of sources and petabytes of text, and enables data connection. It helps to access the right data at the right time.

•Customer Care Service:

Digging techniques are gaining increasing importance in the field of customer care. Organizations spend money on text analysis programs to improve their overall knowledge by accessing text data from a variety of sources such as customer feedback, surveys, customer calls, etc. The main purpose of text analysis is to reduce the response time of organizations and to help deal with customer complaints quickly and effectively.

•Business Intelligence:

Companies and business firms have begun to use scripting techniques as a major part of their business intelligence. In addition to providing valuable information on customer behavior and trends, texting strategies also support organizations to analyze the strengths and weaknesses of their competitors, which gives them a competitive advantage in the market.

•Communication Analysis:

Social media analysis helps to track online data, and there are many text mining tools designed specifically to analyze the performance of social media sites. These tools help to monitor and interpret online-generated text from news, emails, blogs, etc.

We have learnt what Text Mining is all about. Also, learn the process, methods and applications of Text Mine. I hope this blog will help you understand Text Mining.
Many thanks to
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And all.