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Text analytics

76 articles · Page 1

This section gathers the site's articles on text analytics: the software, methods and decisions involved in turning documents, reports and open-ended responses into usable insight. Coverage includes natural language processing and machine learning approaches to text, semantic analysis, automated text analysis and key insight extraction, plus how these techniques fit into business intelligence and market research work. Articles compare text analytics platforms and explain how to choose software, weigh enterprise adoption questions, and look at reported ROI alongside the risks and limitations vendors tend to leave out. Also here: case studies, text mining trends, and guidance on reading business and market research reports critically, including alternatives to spreadsheet-based analysis.

Frequently Asked Questions

What is text analytics?

Text analytics is the practice of analysing unstructured written material — reports, survey responses, documents — to extract structured insight from it. It typically combines natural language processing, machine learning and semantic analysis to identify themes, entities and patterns that would be slow to find by reading alone.

How do I choose text analytics software?

Start from the decisions the output has to support, then check whether a platform handles your document types, languages and volumes. Compare how each tool surfaces evidence behind its findings, how it integrates with existing business intelligence workflows, and what the total cost looks like once setup and review time are included.

What are the main risks of AI text analysis?

The common risks are confident-sounding output that misreads context, bias carried in from the source material or the model, and insights that cannot be traced back to the underlying text. Human review of samples and clear documentation of method are the usual safeguards, particularly when findings feed strategic decisions.