Text analysis organizes or simplifies unstructured textual data such as social media posts, survey responses, reviews on websites, etc., into machine language that can be analyzed. 

In simple words, sentiment analysis is a functionality of Text Analysis that uses Natural Language Processing (NLP) to determine the sentiment behind customer feedback. It classifies feedback as positive, negative, or neutral.

Text Analysis software automates the entire process of analyzing textual data. 

It analyzes structured and unstructured data, identifies hidden emotions, discovers high-priority topics, uncovers issues, and more. Using a Text Analysis tool cuts down the use of multiple solutions for each use case. 

Happy customers help grow company revenue, and happy employees help drive business growth. With the Text Analysis tools, you can also learn whether your customers and employees are satisfied or not. 

By understanding their satisfaction with your organization, you can serve them in a way that will lead to long-term success.

Broadly speaking, Text Analysis tools can be used to

• Analyze customers’ experience with your brand, products, services, etc.

• Analyze a large volume of open-ended text and convert them into insights. 

• Understand how your employees feel about their workplace. Identify issues causing less productivity or dissatisfaction to resolve them effectively and boost employee morale.