Analyzing Document Categories
Let’s consider an example where we have a dataset of news articles categorized into different topics. We want to analyze the distribution of categories within our dataset.
Indexing Data:
PUT /news_articles/_doc/1
{
"title": "Tech Giants Unveil New Products",
"category": "Technology"
}
PUT /news_articles/_doc/2
{
"title": "Fashion Week Trends 2023",
"category": "Fashion"
}
PUT /news_articles/_doc/3
{
"title": "Stock Market Update: Bullish Trends Continue",
"category": "Finance"
}
Performing Term Aggregation
GET /news_articles/_search
{
"size": 0,
"aggs": {
"categories": {
"terms": {
"field": "category",
"size": 10
}
}
}
}
Output:
{
"aggregations": {
"categories": {
"buckets": [
{
"key": "Technology",
"doc_count": 1
},
{
"key": "Fashion",
"doc_count": 1
},
{
"key": "Finance",
"doc_count": 1
}
]
}
}
}
Analysis:
- There are three categories: “Technology,” “Fashion,” and “Finance.”
- Each category has one document associated with it.
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.