Configuring categorization settings
Categorization settings give you control over how the text is categorized, depending on the selected level of classification granularity. You can adjust text categorization according to your business needs, for example, change the analysis granularity to document level if you analyze short tweets. The Topic settings section is available only when the categorization analysis is enabled on the Select Analysis tab.
In the Records panel, click .
Click the Advanced tab.
In the Topic settings section, select the granularity level for the analyzed text:
- Select Sentence Level for high-precision analysis. When you select this option, you analyze each sentence separately. Use this feature when you analyze large units of text (for example, emails, blog entries, and so on).
- Select Document Level to categorize the text as a whole, with no further breakdown. Use this classification when you analyze smaller units of text (for example, Facebook posts or tweets).
Select the criteria for categorizing text:
This setting is available only when you select Use model based topics if available in the Text categorization section of the Select Analysis tab. For the Sentence level granularity, depending on the criteria that you selected, the system always displays only the top category or categories only above the 0.5 confidence score.
- Select Select top N categories to display only the specific number of categories that received the highest confidence score.
- Select Select categories above confidence score threshold to limit the number of detected categories only to those above a specific confidence score threshold.
To switch to rule-based topic detection if the specified confidence threshold is not reached, select Fall back to rule-based topics if confidence threshold is not met.
- Configuring advanced text analytics settings
Configure language detection settings, enable spell checking, and control how the text is categorized, based on various criteria.
- Configuring language detection preferences
You can control how a text analyzer detects languages in the analyzed document. For example, you can enable a fallback language in case your text analyzer does not detect the language when analyzing content that is written in multiple languages.
- Configuring sentiment score range
You can define a sentiment score range to specify the type of sentiment feedback that you receive: positive, negative, or neutral.
- Configuring spelling checker settings
By using the spelling checker, you can categorize the text with a greater confidence score, making the analysis more accurate and reliable.