Merlin Text Analysis (Beta)
The Merlin Text Analysis connector is a powerful AI-driven tool in Tray's automation platform, designed to make analysis and classification capabilities simple to use within workflows.
During the beta phase, the native AI capabilities of this connector are charged as a single task.
OverviewCopy
The Merlin Text Analysis connector is a powerful AI-driven tool in Tray's automation platform, designed to make analysis and classification capabilities simple to use within workflows. It offers a suite of advanced natural language processing operations that can be seamlessly integrated into various automation scenarios.
OperationsCopy
Classify TextCopy
The Classify Text function categorizes a given piece of text into one or more predefined categories based on its content.
Parameters
Text: The input text to be classified.
Categories: The list of categories to classify into, if not predefined in the model.
Examples: is used for in-context learning, where the model is given a few examples to guide its classification process.
In the following example, the workflow iterates over records from a Google Sheet that contains feature titles and descriptions.
The descriptions are provided as input to the Merlin Functions connector to classify each feature into specific categories. A list of categories is supplied to aid the categorization process. For more precise classification, you can also provide examples.
Sentiment AnalysisCopy
Sentiment analysis, also known as opinion mining, is a powerful tool that has a wide range of applications across different industries and domains.
Its primary purpose is to understand the emotional tone behind a body of text, helping organizations and individuals make data-driven decisions.
The sentiment of the text is typically categorized as POSITIVE, NEGATIVE, or NEUTRAL.
In the following example, the workflow is triggered whenever a new customer support ticket is created.
The Merlin Functions connector employs the Sentiment Analysis operation to analyze the data received from the trigger and categorize the sentiments as positive, negative, or neutral.
As depicted in the image below, the step output shows the sentiment categorized as 'NEUTRAL' along with its detailed score.
This detailed score provides actionable insights, enabling effective decision-making in how to respond and utilize the data. For instance, based on a negative sentiment score, appropriate actions like escalating the ticket can be taken if necessary.
