FAQs: Topic spotting

How do I choose appropriate strictness values when entering a topic phrase?

To determine the optimal strictness for a phrase, start with a default setting, then evaluate the captured events and adjust the strictness accordingly. Experimentation is key to finding the right balance between precision and recall.

Longer phrases with more meaningful words often require less strict matching. For example, a three-word phrase might match effectively with medium strictness (two out of three words), while a five-word phrase might only need three matches (medium-low strictness). The ideal strictness depends on the specific phrase and its intended use.

Below are some examples where adjusting strictness can either help or hinder your results, highlighting the importance of tweaking strictness for the best precision/recall tradeoff.

For more information, see Work with a topic, and Work with a phrase.

Examples where raising the strictness will avoid mistakes (increase precision)Click to expand

Defined program phrase	Detected phrase	Topic matched	Selected strictness	Explanation
Called the other day	Called there the other day	Contacted Previously	Medium	“There” provides context that make this irrelevant to the matched topic. Increasing the strictness would deem this occurrence as ‘not a close enough match’ for Contacted Previously.
I called back	I may call back	Contacted Previously	MedLow	“May” provides context that make this irrelevant to the matched topic. Increasing the strictness would deem this occurrence as ‘not a close enough match’ for Contacted Previously.
You’d like to cancel	Cause it wouldn’t be like cancelling	Cancel Mention	MedHigh	The context of “cancel” is different, therefore making this irrelevant to the matched topic. Increasing the strictness would deem this occurrence as ‘not a close enough match’ for Cancel Mention.

Examples where raising the strictiness will miss good occurrences (decrease recall)Click to expand

Defined program phrase	Detected phrase	Topic matched	Selected strictness	Explanation
I called a while ago	Called a little while ago	Contacted Previously	Med-Low	Contacted Previously was correctly matched, despite the extra “little” in the detected phrase. Increasing the strictness would result in classifying this occurrence as “not a close enough match” for Contacted Previously.
I spoke to another person	Person I spoke to	Contacted Previously	Med-Low	Contacted Previously was correctly matched, despite the missing “another” in the detected phrase. Increasing the strictness would result in classifying this occurrence as “not a close enough match” for Contacted Previously.
Want to cancel	Want to just cancel	Cancel Mention	Med-Low	Cancel Mention was correctly matched, despite the extra “just” in the detected phrase. Increasing the strictness would result in classifying this occurrence as “not a close enough match” for Cancel Mention.
Cancel the order	Cancel the money order	Cancel Mention	Med-Low	Cancel Mention was correctly matched, despite the extra “money” in the detected phrase. Increasing the strictness would result in classifying this occurrence as “not a close enough match” for Cancel Mention.

Topic spotting – What is the best way to organize my topics ?

The best way to organize topics is to tag them.

Tags provide a versatile framework for categorizing and classifying topics across a wide range of contexts. Tags create contextual connections between seemingly unrelated topics. By tagging topics, you establish an intricate web of associations, enabling users to discover related information. For example, tags can be used to find topics in Topic Definitions.

Topic spotting – Are email and chat attachments analyzed?

No, email and chat attachments are not analyzed. Speech and text analytics features only analyze the email and chat itself.

Topic spotting – Are there out-of-the-box topics?

The speech and text analytics solution includes a variety of out-of-the-box topics for the following areas of analysis:

Agent behavior
Contact reason
Customer experience

For more information, see Out-of-the-box topics.

Topic spotting – Are topics and intents from bots the same thing?

Although they are similar concepts, topics and bots are unique and serve different purposes in Genesys Cloud.

For speech and text analytics, topics are a collection of phrases that indicate a specific business opportunity that the organization is actively looking for within the interaction transcriptions.

For more information, see About programs, topics, and phrases.

Topic spotting – How do I create and configure a default program?

To create a program and configure it as a default program, you must have administrator privileges.

The default program highlights and transcribes the topics and phrases associated with all of the interactions that are not serviced by another active program.

To create a program, click Admin > QM > Programs and click Create Program. To create a program, click Menu > Conversation Intelligence > Speech and Text Analytics. Click Programs. For more information, see Work with a program.

To configure the specific program as the default program, click Admin > Quality, and click Speech and text analytics settings. To configure the specific program as the default program, click Menu > Conversation Intelligence > Speech and Text Analytics. Click Programs. Select the specific program from the Default program list. For more information, see Speech and text analytics settings.

Topic spotting – How do I generate and use out-of-the-box topics?

To generate and use out-of-the-box topics, you must invoke the create general program API using the following parameters:

Dialect – en-US, es-US, en-AU or en-GB

Mode – merge or skip

For example, /api/v2/speechandtextanalytics/programs/general/jobs.

For more information, see Out-of-the-box topics.

Topic spotting – What are programs used for?

A program is a group of topics that provide instructions to speech and text analytics about the specific phrases and conditions it should focus on.

Programs are essential, since different parts of the contact center may have different business intents of interest.

For example, the retention department can have very specific scripts or processes that do not apply to the rest of the organization. In this case, it would make sense to have a program that includes topics that apply to this specific department and a different program that includes topics that apply to the rest of the company.