Text Analytics

Text analytics is the process of extracting meaning from text data in order to make informed decisions. The purpose of text analytics is to turn unstructured text into structured data that can be analyzed to reveal trends, patterns, and insights.

Text analytics can be used for a variety of tasks, such as sentiment analysis, topic modeling, and text classification. Sentiment analysis is the process of determining whether a piece of text is positive, negative, or neutral. Topic modeling is the process of identifying themes in a piece of text. Text classification is the process of assigning a label to a piece of text.

Text analytics is often used in customer service and marketing applications. For example, it can be used to analyze customer reviews to identify areas of improvement for a product or service. It can also be used to analyze social media data to understand what people are saying about a brand.

Text analytics is sometimes confused with natural language processing (NLP). NLP is a subfield of artificial intelligence that deals with understanding human language. Text analytics is a subset of NLP that focuses on extracting meaning from text data.

Mathematical Approach

Text Analytics can be defined as a mathematical approach to deriving high-quality information from textual sources through the application of natural language processing, statistical learning, and text mining techniques.

The term Text Analytics is also used interchangeably with terms such as text mining, machine learning on text, or predictive text analytics.

Language-based Approach

Text Analytics can also be defined as a language-based approach to extracting actionable insights from text data. This approach relies on a combination of linguistic, statistical, and machine learning techniques to analyze text data.

Mathematical vs Language-based Advantages

There are advantages and disadvantages to both the mathematical and language-based approaches to text analytics.

Advantages of the mathematical approach include:

  • The ability to handle large amounts of data
  • The ability to identify hidden patterns and relationships
  • The ability to make predictions

Advantages of the language-based approach include:

  • The ability to understand the context of text data
  • The ability to identify the sentiment of text data
  • The ability to extract insights from unstructured text data

Leave a Reply

Your email address will not be published. Required fields are marked *

Unlock the power of actionable insights with AI-based natural language processing.

Follow Us

Recent Blog

© 2023 VeritasNLP, All Rights Reserved. Website designed by Mohit Ranpura.
This is a staging enviroment