Introduction 

Text mining is a term that might’ve hit your ears too much recently. It plays a crucial role in data analytics. Talking about the company’s data, there are various types of data that the company receives. However, this data can be brought into use by the company; at the same time, it is a difficult process. Some of the data cannot be easily processed because of the unstructured format. This is where text mining comes. Text mining in data mining helps with the data analysis process. This article will discuss text mining techniques, tools, examples, and also the process.

In this article let us look at:

  1. What Is Text Mining?
  2. Text Mining Techniques
  3. Text Mining Applications
  4. Text Mining Process
  5. Text Mining Tools
  6. Text Mining In Data Mining

1. What Is Text Mining?

Text mining, in other words, is also known as text analysis. In this process, the unstructured data or data that is difficult to read is processed to be utilized by the machine. Text mining usually processes structured data and unstructured data to help process the format of the data. These tactics are used by various businesses that process big data. Tech mining techniques help these businesses processing big data to help them get the outcome for their business. This uses artificial intelligence technologies to help process data, which can help companies to make decisions.

Text Mining application uses natural language processing to give structure to the text. NLP of natural language processing is a machine’s ability to read the text and understand humans’ language. The machine can only operate based on the machine language or the assemble language, but natural language processing enables the machine to respond to the human languages and operate accordingly. In recent years, machine learning and artificial technologies have advanced and are being used by various industries to help make the process efficient. The technology has developed so far that NLP can now efficiently analyze multiple texts without being inappropriate. This is what the text mining definition is. 

2. Text Mining Techniques

There are various text mining techniques:

  • Information Extraction- This process is used to extract useful information from unprocessed or unstructured data. This text mining process focuses on identifying the extraction of attributes, entities. And it also focuses on identifying relationships from semi-structures or unstructured texts. Information extracted using this method is then stored in the database or the storage, which can be then used in the future. 
  • Informational Retrieval- Using this process, relevant and associated patterns are extracted based on specific sets of words. This process takes the help of a different algorithm and then tracks the users’ behavior and then discovers the relevant data. Categorization- In this process, language texts are assigned to a set of data, retrieved upon the searches. These process categories co-relating data into natural language texts, which can be later viewed. 
  • Clustering- Clustering is another crucial text mining process, which arranges the text in various groups and subgroups. However, the process might seem easy, but it isn’t easy. This is so because one will have to add certain text sets to groups and subgroups that do not have any names. They should form a network pyramid that forms the same overall meaning. 
  • Summarization- This process automatically generates compressed text, which is informational. This uses multiple text sources to analyze and summarize the text contained in these multiple text sources. However, this process might collect various parts of the texts to keep the overall meaning and the intent of the text the same.

3. Text Mining Applications

Various text mining applications are used worldwide. Let’s discuss some text mining applications one should know about.

  • Risk Management- Analyzing risk is one of the important steps when doing a business. Many businesses fail because they fail at proper risk management. Text mining in python is used, which helps gather data, helping businesses analyze the risk of their decisions.
  • Customer Care Service- Better customer care service is available because of the text mining techniques. These use textual data received during feedback, calls, emails, and use to serve the customers.
  • Fraud Detection- With the advancement in technology, one can easily identify any fraud texts. Text mining has helped achieve that. With various data from previous frauds, one can now easily identify any fraud taking place.
  • Business Intelligence- Businesses’ data is their backbone, which can be used for or against the company. If the company knows how to analyze and properly use their data, this can help them in the long run. Businesses can tell from the customer feedback and queries about what their customers need.
  • Social Media Analysis- It also helps analyze the post their likes, comments, shares, etc., which helps increase your post reach.

4. Text Mining Process

The text mining process involves the following steps-

  • The very first process involves collecting unstructured data. These can be from sources such as websites, pdf, emails, and blogs.
  • Removing unwanted data takes place then. This involves data cleansing, which removes all the unwanted parts from the data and extracts valuable information.
  • Then all the extracted data that is unstructured is converted to structured data.
  • Now the data is analyzed to find various hidden patterns and also process the outcomes.
  • The data extracted is then stored safely to be used for various purposes and future use.

5. Text Mining Tools

Various text mining tools can be used for the process. These tools use NLP, machine learning, and artificial intelligence to process structured data from the raw data. Some of the text mining tools are given below.

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

6. Text Mining In Data Mining

Text mining is the process of data mining and data analytics, which helps boost the process. However, there is some difference between text mining and data mining. Data mining is used to find patterns and extract useful data from various large data sets. Whereas in text mining, the data is processed from various text documents. In data mining, the data is processed from the pre-existing database and spreadsheets, which gathers information, whereas, in text mining, the text is used to get high-quality information.

Conclusion

Text mining uses NLP and various advanced technologies to help get structured data. Text mining is now widely used by various companies who use text mining to have growth and to understand their audience better. There are various examples in the real-world where text mining can be used to retrieve the data. Various social media platforms and search engines, including Google, use text mining techniques to help users find their searches. This helps with getting to know what the users are searching for. Hope this article helps you understand various text mining algorithms, meaning, and also techniques.

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Also, Read

What is Data Mining? Working, Uses, and Advantages

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