What is Text Mining: Techniques and Applications

INTRODUCTION

Text Mining is among the most important methods to analyze and process unstructured data that makes up around 80% of world's data. Nowadays, the majority of businesses and institutions collect and store huge quantities of information in data warehouses and cloud platforms . This data is growing exponenWhat is Text Mining: Techniques and Applicationstially every minute as data is poured into the system from a variety of sources.

In the end, it is a major challenge for businesses and other organizations to manage as well as process massive amounts of textual information using traditional tools. Learning to use data science tools can help you conquer the difficulties. We'll discuss text mining.

What is Text Mining?

As per Wikipedia, "Text mining, also referred to as text data collection mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text." The definition strikes the core of text mining: to dig into unstructured data in order to discover relevant patterns and insight for analyzing textual data sources.

Text mining combines the tools of data mining, information retrieval machine learning, statistics and computational linguistics therefore, it is a multidisciplinary field. Text mining is concerned with natural language texts that are stored in unstructured or semi-structured formats.

The five main steps with text mining include:

Collecting unstructured data from a variety of sources, including web pages, plain text pdf files email, blogs, and emails just to mention a few.

Find and eliminate anomalies in data through cleaning and pre-processing. Data cleansing lets you keep and store the important information that is hidden in the data as well as to determine the source of certain terms.

In this regard, you can get several text mining tools as well as text mining software.

Convert all information that is extracted from unstructured information into structured formats.

Examine patterns in the data by using analysis of patterns in the data using Management Information System (MIS).

All the important information in a secure database to facilitate trend analysis and help improve the process of making decisions for the business.

Text Mining Techniques

Text mining techniques can be understood by the process involved in extracting text from the internet and gaining the insights it provides. Text mining techniques typically utilize different tools for text mining and software for their implementation. Let's take a look examine the different text mining methods:

1. Information Extraction

This is probably the most well-known technique for mining text. Information exchange is the process of separating relevant information from huge textual information. The text mining method concentrates on the detection of attributes, entities and their relationships from unstructured or semi-structured texts. What information is retrieved is kept in a database for later accessibility and retrieval. The accuracy and relevance of the results are analyzed and checked by using recall and precision processes.

2. Information Retrieval

Information Retrieval (IR) refers to the process of finding relevant patterns and patterns from a particular group of words or phrases. When using this method of mining text, IR systems make use of different algorithms to observe and monitor user behavior and identify relevant data in line with. Google as well as Yahoo the search engine are two of the most popular IR systems.

3. Categorization

It is one of the methods of mining text that is a type of "supervised" learning wherein normal textual content is assigned to the predefined topics in accordance with their content. This is why categorization, or more precisely Natural Language Processing (NLP) is the process of acquiring texts and analyzing them to find the most appropriate subjects or indexes for every document. The method of co-referencing is widely used in NLP to identify relevant abbreviations and synonyms from textual information. Nowadays, NLP has become an automated method that can be employed in a variety of situations, from personal commercials, to spam-filtering and categorizing web pages according to an orderly structure, and many more.

4. Clustering

Clustering is among the most essential methods of mining text. It is a method to find fundamental patterns in textual data and arrange them into appropriate clusters or subgroups to be further analyzed. One of the major challenges in the process of clustering is to construct meaningful clusters of textual data , without having prior knowledge about the AI training dataset. A cluster analyzer is typical text mining tool that aids in data distribution , or acts as a pre-processing process for other algorithms for text mining running on clusters detected.

5. Summarization

Text summarization is the process of creating automatically an uncompressed version of a text that contains important data for the end-user. The purpose of this technique is to search through a variety of sources of text to create summary of texts that include significant amounts of information in a compact format while keeping the purpose and meaning of the original texts basically the same. Text summarization combines and integrates the different methods used for classification of text, including neural networks, decision trees or regression models, as well as Swarm Intelligence.

Applications Of Text Mining

Text mining methods and text mining tools are rapidly gaining traction in the market, from the medical and academic worlds to social media and businesses platforms. This is resulting in numerous applications for mining text. Here are some text mining tools that are being used around the world currently:

1. Risk Management

One of the main causes of failures in the business world is the absence of a thorough or adequate risk analysis. Integrating risk management software that is powered by technology that mines text like SAS Text Miner can help businesses stay abreast of the latest developments in the business world and enhance their capabilities to limit risks. Because the tools and technologies for text mining are able to gather pertinent information hundreds of sources of text data and make connections between extracts, it permits businesses to gain access to the correct data at the right time which can enhance the whole processes of risk control.

2. Customer Care Service

Text mining techniques, especially NLP are gaining importance in the area of customer service. Businesses are investing in software for text analysis to improve their customer experience overall by analyzing textual information from a variety of sources, including surveys, feedback from customers and calls from customers and more. Text analysis is designed to decrease the time to respond for the business and assist in addressing customer complaints quickly and efficiently.

3. Fraud Detection

Text analytics, backed by text mining methods can be a great chance for websites that can collect the majority of their information in text format. Finance and insurance companies are taking advantage of this potential. By combining the results of analysis of text with pertinent structured data, these companies can now process claims in a timely manner as well as to identify and stop fraud.

4. Business Intelligence

Businesses and organizations have begun to utilize the use of text mining methods as component of their intelligence for business. In addition to providing deep insight into the behavior of customers and trends, these techniques can help businesses determine how they compare to their competitors and give them a an edge on the market. Text mining tools like Cogito Intelligence Platform and IBM text analytics can provide information on the effectiveness of strategies for marketing, the latest trends in market and customer behavior as well as other such.

5. Social Media Analysis

There are numerous text mining tools specifically designed to analyze what happens on social platforms. They assist in tracking and analyze the text that is generated online , such as news articles emails, blogs and more. In addition, these tools are able to efficiently analyze the amount of posts, likes and followers for your brand via social platforms, helping you understand the way people interact with your company's online content. This analysis will allow you to know what's trending and what's not for your market.

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