We send texts to everyone regularly. The texts are in the form of formal messages to our office colleagues and fellow employees. At times the texts are directed towards our near and dear ones. These texts can be categorized either into structured or unstructured data. Text mining is the process through which unstructured data is converted into structured data. 

Through text mining, we derive the meaning of the unstructured data that is provided to us. The task of deciphering the data from unstructured data is given to the function of text mining. 

Categories of Text Mining

Structured data

This data is standardized and divided into a tabular format. It has many rows and columns. The process of storing and managing data becomes easier. This happens because the data is presented systematically. Text Mining ensures data that can be easily read and understood. It includes a database of names, addresses, contact numbers and other information. 

Unstructured data

This data does not have a predefined data format. It can possess any type of information. The information before the process of text mining takes place is known as unstructured data. The information can range from texts, social media messages, product descriptions and audio-video files. These data are called unstructured data because their source or origin is unidentified. Only after text mining, we can determine the origin of the aforementioned data files. 

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Semi-structured data

As the name suggests, this data is a blend of structured and unstructured data formats. It is a hybrid form of text mining. Here some organization and systematic representation are present. But the database is not organised as properly to call it an understandable database. The information stored still requires some deciphering through text mining. 

While it has some organization, it doesn’t have enough structure to meet the requirements of a relational database. Examples of semi-structured data include XML, JSON and HTML files.

Text Mining vs Text Analytics

Although Text Mining and Text Analytics are used simultaneously, their meanings are different. Through machine learning, statistical analysis and understanding of information technology, text mining can help in deciphering important codes and information about a process. The results that are obtained in the process help in the communication of information from one source to another. Thereby text mining is considered to be an important part of the text analysis portfolio. 

Techniques of Text Mining

There are many techniques associated with text mining. Following are some of the most important techniques of text mining-

Information retrieval

Information retrieval (IR) refers to the process of retrieving important information or documents. In this process of text mining, there is a predefined set of questions and queries. Data Analysis and Algorithms are used to track consumer data. This technique is majorly used in platforms like Google. Here through text mining, important information can be discovered and further steps can be taken to put the information to proper usage. There are many sub-techniques to the main primary techniques. Following are some sub-techniques to the process of Information Retrieval- 

Tokenization

This is the process of breaking out long-form text into sentences and words called “tokens”. The words are broken down through text mining and are used in clusters. The document obtained through text mining can further be used for matching information and elements. 

Stemming

This refers to the process of separating the prefixes and suffixes from words to derive the root word form and meaning. In Text Mining all the words assume significance. Therefore to clearly understand the depth of the word, the prefix and suffix are to be understood clearly and concisely. Only then can the true meaning of the word or the sentence in question be deciphered. Reducing the size of the index files through text mining is also an important function. This technique improves information retrieval by reducing the size of indexing files.

Natural language processing (NLP)

Natural language processing is the second task in the text mining process. Here computational linguistics help in developing Important methods. Several techniques are utilized to understand the human language and its practical orientation and implementation through both written and verbal format. Through NLP in Text Mining, reading and deciphering important information becomes easier. Following are some of the most important sub-tasks under this category-

Summarization

This technique provides a synopsis of long pieces of text. Summarizations of long texts are available. This concise summary available through text mining gives a brief idea about the introductory part of the topic of discussion. It includes the most essential parts of the document. 

Part-of-Speech (PoS) tagging

This technique assigns a tag to every token in a document based on its part of speech—i.e. denoting nouns, verbs, adjectives, etc. This step enables semantic analysis of the unstructured text.

Text categorization

This task, which is also known as text classification, is responsible for deciphering text documents. After the encryption of the documents through text mining is over, necessary steps are taken to classify them into predetermined tasks and objectives. The classification takes place through the topic allocation. Synonyms and Abbreviated versions of the topic are helpful in better analysis. Text mining, therefore, allows proper encryption of information. 

Sentiment analysis

This task detects positive or negative sentiment from internal or external data sources. Text mining allows you to channelize customer preferences. Through the different techniques, you can gauge customer feelings. Customer preferences and their feelings about different brands, products and services can be identified through text mining. It is commonly used to provide information about perceptions of brands, products, and services. These insights can propel businesses to connect with customers and improve processes and user experiences.

Summary

Text Mining is therefore an integral process of data synthesis. It is considered to be one of the core concepts in understanding Information Technology

Frequently Asked Questions (faqs)

What is the basic concept behind text mining?

Text mining is the process through which unstructured data is converted into structured data. 

Which sector does the study of Text Mining fall under?

The study of Text mining falls under the sector of Information Technology. 

What are the primary subtasks under Natural Language Processing (NLP)?

The primary subtasks under Natural Language Processing (NLP) are-

  • Summarization
  • Part-of-Speech (PoS) tagging
  • Text categorization
  • Sentiment analysis

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