Knowledge Discovery 25.1
Knowledge Discovery Expert

# Term Analysis

Term analysis of a document or set of documents returns statistical information on the terms in the documents. The information can in turn be used to perform conceptual operations, and it is integral in additional functionality such as categorization, clustering, and profiling.

The `TermGetAll` action allows you to list the terms that are stored in the Content component. You can also view information about terms in the indexed data on the Terms tab on the Performance page in the Monitor section of Knowledge Discovery Admin.

You can also return additional information about the terms in your documents:

- Display Details About the Terms in the Content component

- Find the Most Common Terms

- Find the Documents with the Most or Least Terms

## Display Details About the Terms in the Content component

You can return details about the types of terms that occur in Content, by setting the `TermAnalysis` parameter to `True` in the `TermGetAll` action.

`TermAnalysis` returns the following information, in `<autn:termanalysis>` tags:

Detail | Description
--- | ---
`Terms` | The total number of terms in Content.
`Numeric` | The number of purely numeric terms. For example 13570.
`Alphanumeric` | The number of alphanumeric terms. This count excludes purely numeric terms. For example A739B, 12a.
`Multibyte` | The number of terms that include at least one multibyte character. For example, café or ψάρι.
`Dococcs logn` | The number of terms that occur in between `2`(n-1)`+1` and `2`n documents. For each item, the number in quotes (`""`) is the value of `n`. For example: `<autn:dococcs logn="0">` is the number of terms that occur in 20 documents (1 document). `<autn:dococcs logn="1">` is the number of terms that occur in between 20+1 and 21 (2) documents. `<autn:dococcs logn="2">` is the number of terms that occur in between 21+1 and 22 (3-4) documents. `<autn:dococcs logn="3">` is the number of terms that occur in between 22+1 and 23 (5-8) documents.
`Length len` | The number of terms of each length. NOTE: This length counts a multibyte character as length 1. For example, the word café is counted as length 4, even though the UTF-8 string is 5 bytes.

To return only the analysis information, send `TermGetAll` with `TermAnalysis` set to `True`, and `MaxTerms` set to `0`. For an example response, see Example TermGetAll TermAnalysis Response.

## Find the Most Common Terms

You can use the `TermGetAll` action to find the most common terms in Content.

Set `Type` to `TrueOccs` to return terms in order of the number of times they occur in Content. The most common terms are listed first.

For example:

    action=TermGetAll&Type=TrueOccs&MaxTerms=100

This action returns the 100 most common terms in Content. For example:

Copy

    <autnresponse>
       <action>TERMGETALL</action>
       <response>SUCCESS</response>
       <responsedata>
          <autn:number_of_terms>3675990</autn:number_of_terms>
          <autn:term document_occurrences="6117" total_occurrences="11798">ON</autn:term>
          <autn:term document_occurrences="4973" total_occurrences="11690">NEW</autn:term>
          <autn:term document_occurrences="5387" total_occurrences="9964">FIRST</autn:term>
          <autn:term document_occurrences="4923" total_occurrences="9637">YEAR</autn:term>
          <autn:term document_occurrences="4060" total_occurrences="9237">STATE</autn:term>
          <autn:term document_occurrences="4740" total_occurrences="8335">TIME</autn:term>
          <autn:term document_occurrences="3862" total_occurrences="7939">US</autn:term>
          <autn:term document_occurrences="4801" total_occurrences="7606">TWO</autn:term>
          <autn:term document_occurrences="3936" total_occurrences="7587">NAME</autn:term>
          <autn:term document_occurrences="3133" total_occurrences="7485">UNIT</autn:term>
    ...

where,

`document_occurrences` | is the number of documents that the term occurs in.
`total_occurrences` | is the total number of times that the term occurs in all documents in Content.

TIP: By default, `TermGetAll` returns the stemmed terms. To return the unstemmed forms, you can set the `Stemming` parameter to `False` in the `TermGetAll` action. See Stemming With Term Actions.

## Find the Documents with the Most or Least Terms

You can use the FieldText parameter of an action along with document metadata fields to restrict the documents returned. For example:

    action=Query&FieldText=EQUAL{1}:autn_distincttermsperdoc

This action returns the documents that contribute to the count in the `<autn:distincttermsperdoc logn="1">` tag.

    action=Query&FieldText=EQUAL{10}:autn_termsperdoc

This action returns the documents that contribute to the count in the `<autn:termsperdoc logn="10">` tag.

You can use this method to investigate potentially corrupted documents. For example, documents in the `<autn:distincttermsperdoc lognx="20">` segment have roughly 220 (over one million) distinct terms. The Oxford English Dictionary second edition has approximately 600,000 definitions, so an English document in this segment has many more distinct terms than there are English words. This document might contain a high number of garbage terms, or numeric and alphanumeric terms.

At the other end, documents in the `<autn:distincttermsperdoc lognx="0">` segment have only one distinct term. This segment might also indicate a problem, or missing data.

Related Topics

- Use TermGetAll to Diagnose Problems

- Stemming With Term Actions

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Version 25.1 | Last updated January 2025

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Product: Knowledge Discovery 25.1

Topic Title: Term Analysis