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Difference between Data mining and Text mining

Data mining can be understood as a process of data extraction from a huge data set. The data is extracted to acquire knowledge about certain data sets to be further used for learning and processing purposes.

Data mining involves the following steps:

  1. Business Understanding: Business understanding refers to a process of comprehending each feature of a topic and work.
  2. Data Selection: It is used to pick the best data set for performing data extraction.
  3. Data Preparation: It prepares the extracted data to undergo further improvement.
  4. Modeling: It remodels the input data based on user requirements.
  5. Evaluation: It thoroughly reviews the complete process to check for possible faults or data leakage within the process. It plays an important role in data
  6. Deployment: Once everything is evaluated, the data is ready for deployment and can be further utilized.

Applications of Data Mining

Data mining vs Text mining
  • Market Analysis

Market analysis is one such application of data science that helps analyze the current status of the market. As a result, it enables an individual in decision-making in terms of investments and business strategies for generating profit.

  • Fraud Detection

Frauds can be easily detected with the help of fraud detection by extracting more and more information related to any particular instance and then formulating a decision whether it is legal or illegal.

  • Customer Retention

It extracts customer's information based on their interests and offers them exciting deals to buy any particular product. These strategies not only help in providing a high level of customer satisfaction but also maintain a healthy relationship with them.

  • Science Exploration

With the help of data mining, we can extract previous experiments or test case's knowledge and further utilize it to work proficiently. In this way, the errors can be minimized by learning from preceding mistakes and utilized for producing better results.

Text Mining

Text Mining is also known as text data mining. It refers to the process of extracting high-quality data from the text. High-quality data is usually extracted through the discovering of patterns and trends such as statistical pattern learning.

Text analysis includes pattern recognition, information extraction, information retrieval, data mining techniques involve association analysis, visualization, and predictive analytics.

Text Mining comprises a wide range of methods; the primary three methods are given below.

Data mining vs Text mining
  1. Keyword-based technologies
  2. Statistics technologies
  3. Linguistic based technologies

Keyword-based technologies

In keyword-based technologies, the input is based on the keywords selected in the text extracted as a series of character strings.

Statistics technologies

Statistics technologies refer to the system which is based on machine learning. It has a training set of documents used as a model to categorize and manage text.

Linguistic based technologies

Linguistic-based technologies are a method based on a language processing system. The output f the text analysis gives an understanding of the structure of the text, logic, and grammar employed.

Application of Text Mining

Risk Management

Risk management is the process of identifying risk, quantifying that risk, and then employing different types of strategies to manage that risk. Preliminary risk analysis is usually a primary cause of failure of any industry. Primarily in the financial industry, where adoption of risk management software based on text mining can enhance the capability to reduce risk.

Customer care services

Customer service is the act of taking care of the customer's needs by providing and delivering professional, helpful, high-quality service and assistance before, during, and after the customer's requirements are met. Nowadays, text analytics software is adopted to enhance customer experience using various sources of information such as trouble tickets, surveys, and reviews to improve the management, quality, and speed in resolving problems.

Difference between Data Mining and Text Mining

Data mining vs Text mining
Data Mining Text Mining
Data mining is a process to extract useful information from huge datasets. Text Mining is a part of data mining that includes the processing of text from huge documents.
In data mining, we get the stored data in a structured format. In text mining, we get the stored data in an unstructured format.
It allows the mining of mixed data. It allows mining of text only.
Data processing is done directly. Data processing is done linguistically.
It is a homogeneous process. It is a heterogeneous process.
Pre-defined databases and sheets are used to collect the information. The text is used to gather high-quality data.
The statistical method is used for data evaluation. Computational linguistic principles are used to evaluate the text.

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