Analysis
The significant terms aggregation returns the same results as the term aggregation in this example because each document title contains only one significant keyword.
Real-World Use Cases
- Text Classification: Identifying significant terms within text documents can aid in text classification tasks by highlighting keywords or phrases that are indicative of specific categories or topics.
- Sentiment Analysis: Analyzing significant terms in textual data can help detect sentiment trends by identifying frequently occurring words or phrases associated with positive or negative sentiment.
- Trend Detection: Using the Significant Terms aggregation, you can detect emerging trends or topics within a dataset by identifying terms that are significantly more frequent over a specific time period.
Analyzing Text Data with Term and Significant Terms Aggregations
Elasticsearch provides powerful tools for analyzing text data, allowing users to gain valuable insights from unstructured text documents. Two essential aggregations for text analysis are the Term and Significant Terms aggregations. In this article, we’ll explore what these aggregations are, how they work, their use cases, and how to implement them with examples and outputs.