Introduction
In the real world, the data can be seen in two different categories. You can learn about how big data works through this link. They are structured and unstructured data. Structured data are like table formats, text, etc. At the same time, Unstructured data includes images, audio, video, documents, emails, customer correspondence, tweets, and other formats. In these current technology developing days, the amount of variety of data is growing largely. Analyzing these types of data needs so many concepts and tools. Among them, one of the major types of data is text. Text is one of the common types of data that is used in our daily life. Understanding how to deal with this type of data is very important. To do this, many analytic techniques have been developed, called text analytical tools. After undergoing several data analysis techniques, we will finally get the insights called information. This output is called the extracted information. We will learn more about this extracted information, how looks, etc.
Understanding the extracted Information
After applying so many data analysis techniques such as lexical/morphological analysis, syntactic analysis, semantic analysis, and discourse-level analysis, we will get final outputs or insights as a form of information. This formatted information is used for fetching useful insights to develop our business. The information after applying some tagging and markup, we will get the following kinds of information.
- Terms:
Also called Keywords, they are used for querying useful information.
- Entities:
Usually called “Named Entities”. Defined as specific examples of abstraction.
Example: John Doe can be considered a name of a person. Here Name of a person is called Entity. And Similarly, March o3, 2022, can be considered a Date. Here date is considered as a Named Entity.
- Facts/Relationships:
Tells how two words are related and majorly indicates what/who/where relationships between two named entities.
Example: John Doe is an employee of the company ABC. Here asking who is the employee of ABC results John Doe, etc.
- Events:
Events describe the state of action, usually contain a time dimension, and often cause facts to change.
Example: Change in management within a company, etc.
- Concepts:
Concepts are considered as ideas or thoughts. These are described by a set of words and sentences that indicate a specific concept.
Example: The concept “unhappy” includes a set of words such as angry, disappointed, not satisfied, didn’t get a callback, confused, time waste, etc.
- Sentiments
Sentiments are used to find the feeling or emotions that are underlying the text. Sentiment analysis is a huge concept that can be implemented using the concept of machine learning and deep learning concepts.
Example: Happy, Bad, Not Good, etc.
And not but not least, Taxonomies, Taxonomies is a way of organizing information into a hierarchical fashion/relationship. It majorly deals with how the information is categorized, etc. These taxonomies also play a very important role in natural language processing, especially in the case of Recommendation Systems. Taxonomy actually uses synonyms and alternate expressions in order to organize the information into categories.
Reference - Big Data For Dummies, A Wiley Brand.
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