What is text analytics and provide a practical example?
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What is text analytics and provide a practical example?
Text Analytics Examples This includes analyzing product and service reviews to see how your customers evaluate your company, processing the results of open-ended responses to customer surveys, or checking out what customers say about your brand on social media.
What can you do with text analytics?
Companies use text analysis tools to quickly digest online data and documents, and transform them into actionable insights. You can us text analysis to extract specific information, like keywords, names, or company information from thousands of emails, or categorize survey responses by sentiment and topic.
Where are text analytics used?
For example, text analytics can be used to understand a negative spike in the customer experience or popularity of a product. The results of text analytics can then be used with data visualization techniques for easier understanding and prompt decision making.
What does text analytics include?
Text Analytics is the process of drawing meaning out of written communication. In a customer experience context, text analytics means examining text that was written by, or about, customers. You find patterns and topics of interest, and then take practical action based on what you learn.
What is text data example?
Examples include call center transcripts, online reviews, customer surveys, and other text documents. This untapped text data is a gold mine waiting to be discovered. Text mining and analytics turn these untapped data sources from words to actions.
How do I do text analytics?
There are 7 basic steps involved in preparing an unstructured text document for deeper analysis:
- Language Identification.
- Tokenization.
- Sentence Breaking.
- Part of Speech Tagging.
- Chunking.
- Syntax Parsing.
- Sentence Chaining.
What is text mining and analytics?
Text mining (also referred to as text analytics) is an artificial intelligence (AI) technology that uses natural language processing (NLP) to transform the free (unstructured) text in documents and databases into normalized, structured data suitable for analysis or to drive machine learning (ML) algorithms.