When you perform a significant terms aggregation in Elasticsearch, it analyzes a field in a set of documents and identifies the terms that are most significant or interesting within that field, based on their statistical significance. Here’s how it works:
1. Elasticsearch analyzes the field to extract the terms: Before performing the significant terms aggregation, Elasticsearch first analyzes the specified field in all of the documents to extract the terms. The analysis process may include tokenization, stemming, and other text processing techniques, depending on the configured analyzer.
2. Elasticsearch calculates the statistical significance of each term: Next, Elasticsearch calculates the statistical significance of each term in the analyzed field, based on a statistical model that takes into account the frequency of the term in the field, the frequency of the term across all fields, and the size of the field and the overall corpus. This model helps to identify terms that are significant or interesting within the context of the documents being analyzed, rather than just the most common or rare terms.
3. Elasticsearch returns the significant terms with their significance scores: Finally, Elasticsearch returns a list of the terms that are most significant or interesting within the analyzed field, along with their significance scores. The terms can be sorted by their score, or filtered by a minimum score threshold.
For example, let’s say you have an index of customer reviews for products, and each document has a “review_text” field that contains the text of the review. You could perform a significant terms aggregation on the “review_text” field to identify the words or phrases that are most significant or interesting within the set of reviews. Elasticsearch would then calculate the statistical significance of each term in the “review_text” field, and return a list of the most significant terms with their significance scores.
Significant terms aggregations can be a powerful tool for identifying the most important topics or themes within a set of documents. By focusing on the terms that are most significant or interesting, you can gain insights into the topics and themes that are most important to your users or customers, and use that information to improve your products or services.