Overview of Text Analysis Software

Text analysis, also described as text mining or text analytics, refers to the process of automatic reading and understanding text data using computer software. We can define text analysis as “The process of getting the information and meaning from text data (i.e. emails, phone transcript, surveys reports, customer reviews, and other documents) using computer software.”  

What is Text Analysis Software?

Text analysis software is software that helps to take insight from both unstructured and structured text data. Text analysis software helps in text identification, text extraction, text parsing, text categorization, entities, relations, and events. 

How Does Text Analysis Software Work? 

Text analysis software works on the concepts of Deep learning and Natural language processing:

  • Deep Learning: Deep learning is a machine learning technique where we work to make computers think like humans. Deep learning basically uses an algorithm called ‘Neural Networks’. Neural networks are a series of algorithms that work to achieve the cognitive skills (i.e. problems solving, decision-making, attention, and processing) of humans. Eventually, using deep learning concepts, text analysis software works and reads text data in a similar way to the human brain.
  • Natural Language Processing (NLP): Natural language processing is the ability of computer software to acknowledge human language whether the language is spoken or written.  This method is very helpful in text analysis and using natural language processing, text analysis software is able to read and understand the text data and makes sense of it.

Why is Text Analysis Software Important?

  1. Personalizing Customer Experience:  You can use text analysis software to take insight into huge text-based data and if you belong to a business. So it can provide you with an idea about your customer’s choices, buying habits, and all.
  2. Record Management:  Text analysis software is also helpful in automating patient record management, categorization, and searches of documents.
  3. Topic Clustering: Text analysis software let users allow to classify data into topics and it helps users in content targeting and optimization.
  4. Graphical Data Presentation: Text analysis software represents data in the form of graphic format.
  5. Document Filtering: Text analysis software let users allow retrieve any particular document(s) from a huge number of documents.