What is Text Analytics?
Text Analytics is a process of analyzing and understanding written or spoken language. It employs computer algorithms and techniques to extract valuable information, patterns, and insights from extensive textual data. In simpler terms, text analytics empowers computers to understand and interpret human language.
In simpler terms, text analytics helps computers understand and interpret human language. Here’s a real-world example to illustrate text analytics: Let’s say a company receives customer reviews for its products online. These reviews can be a goldmine of information, but it’s not feasible for humans to read and analyze thousands of reviews manually. This is where text analytics comes in. The text analytics system can automatically analyze the reviews, looking for patterns and sentiments. It can identify common words or phrases that customers use to express satisfaction or dissatisfaction. For example, it might recognize that words like love, great, and excellent often appear in positive reviews, while words like disappointed, issues, and poor may appear in negative reviews.
What is Text Analytics ?
In a world filled with words, from social media posts to online reviews, understanding what they mean on a large scale is no easy task. That’s where text analytics comes in—a powerful tool that helps us make sense of all this information.
In this article, we’ll take a closer look at text analytics, breaking down what it is, Understanding How it works, its applications, and Techniques.
Table of Content
- What is Text Analytics?
- Why is Text Analytics Important?
- What Text Analytics Can Do?
- How Text Analytics Work?
- Language Identification
- Tokenization
- Sentence Breaking
- Part of Speech Tagging
- Chunking
- Syntax Parsing
- Sentence Chaining
- Various Text Analytics Techniques
- Application of Text Analysis
- The Impact of Text Analytics
- The Future of Text Analytics