Text analysis
Text analysis is the computational examination of textual content to extract information, identify patterns, or derive meaning.
Elasticsearch in Action, Second Edition
Text analysis is the processing of text into analyzed linguistic representations to support meaning-aware search and retrieval.
- How it is used
- Prepares text fields and full-text queries for relevant matching in Elasticsearch search.
- How deep it goes
- Explains analyzer pipelines through tokenization, normalization, filters, built-in analyzers, language analyzers, and custom analyzers.
- Examples
- Uses a movie review, K8s and emoji searches, book-title matching, and The Godfather term-query mismatch.
- What you can do after
- Apply it via Elasticsearch analyzers to configure text fields for flexible full-text search or exact keyword matching.
Read how Elasticsearch in Action, Second Edition explains text analysis →
Starting Data Analytics with Generative AI and Python
Text analysis is the computational examination of textual content to identify linguistic patterns, themes, frequencies, or other meaningful properties.
- How it is used
- Uses textual content to extract business insights, patterns, themes, frequencies, and customer opinions.
- How deep it goes
- Explains practical tasks and data-quality problems, but stops short of detailed linguistic algorithms or implementation workflows.
- Examples
- Analyzes customer-review comments for sentiment, topics, word frequencies, themes, complaints, and patterns.
- What you can do after
- Apply it to perform initial review-text analyses, then validate findings against raw records and suitable analytical tools.
Read how Starting Data Analytics with Generative AI and Python explains text analysis →
Related concepts
- text analyzeruses · A text analyzer is a component that transforms text into terms or other linguistic representations for indexing and searching.
- clusteringincludes · Clustering is the technique of grouping data items according to similarity or shared characteristics.
- machine learninguses · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
- tokenproduces · A token is a discrete unit of text, such as a word, word fragment, character, or symbol, represented and processed as an element by a language model.
- structured extractionincludes · Structured extraction is the process of identifying specified information in source content and representing it as organized data in a defined structure.
- keyword fieldcontrasts with · A keyword field is an Elasticsearch field type that stores an entire value as a single unanalyzed term for exact matching, sorting, filtering, and aggregations.
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