Sentiment Analysis

Release version: Australia

Updated March 12, 2026

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Summary of Sentiment Analysis

Sentiment Analysis in ServiceNow’s Task Intelligence for Customer Service helps you understand customer emotions by evaluating case and email text. This feature enables agents and managers to deliver more empathetic and effective service by identifying the sentiment of new and ongoing cases in English. It supports prioritization, routing, case monitoring, and coaching opportunities to improve customer experiences and reduce escalations.

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Key Features

Sentiment Evaluation at Case Creation: Analyzes subject lines, email bodies, and case descriptions to assign a sentiment label (Positive, Neutral, Negative) with a confidence score, stored in the Original sentiment field.
Sentiment Evaluation on Case Updates: Assesses customer replies and comments to update the Current sentiment field, tracks sentiment changes over time, and classifies trends as Improving, Declining, or Neutral.
Pre-trained Machine Learning Model: Automatically predicts sentiment using a pre-trained model, with results stored in the Predictor Results for Task table for transparency and monitoring.
Prediction Feedback and Monitoring: Stores prediction results in the mlpredictorresults table accessible by users with mladmin role, enabling oversight of prediction accuracy and handling of skipped or failed predictions.

Practical Benefits for Customers

Enable agents to prioritize cases based on current sentiment, focusing on emotionally sensitive issues promptly.
Allow managers to route cases to agents with appropriate empathy skills and reassign cases to prevent escalation.
Provide insights for coaching by identifying cases that ended negatively, supporting continuous improvement in service delivery.
Track sentiment trends over time to monitor case progress and customer satisfaction dynamically.

Sentiment Analysis can help you gauge customer emotions, enabling you to provide more empathetic and compassionate customer experiences.

Use the sentiment analysis feature included with Task Intelligence for Customer Service to:

Evaluate email and case text.
Identify the current sentiment of new cases.
Identify the ongoing sentiment of updated cases.
Display this information to agents and managers.

Agents can use current case sentiment to prioritize their work and ongoing sentiment as it trends over time to see if cases are moving in the right direction.

Managers can use sentiment to route cases to agents with the right empathy skills, monitor cases and reassign as needed, and avoid escalations. Manager can also identify coaching opportunities by looking at cases that ended on a negative sentiment.

Note:

In the Australia release, the sentiment analysis feature can predict sentiment for cases created in English.

Sentiment analysis machine learning models

Sentiment analysis uses a pre-trained machine learning model to evaluate email and case text and predict sentiment. This analysis takes place when a case is created and when it is updated by the customer.

Table 1. Sentiment analysis for cases
Cases scenario	Description
When a case is created	The sentiment analysis model evaluates the following text to make a prediction: Text in the subject line and body of emails. Text in the short description and description of cases. If the model can make a prediction, it returns the following information: A sentiment label and corresponding sentiment level. Positive (1.0) Neutral (0.5) Negative (0.0) A confidence level for the prediction. If the model can make a prediction, the sentiment is added to the Original sentiment field. If the model can't make a prediction, the Original sentiment is not set. This system stores the sentiment prediction information in the Predictor Results for Task table.
When a case is updated	The sentiment analysis model evaluates the following text to make a prediction: The text from the body of a reply email. Comments that a customer adds to the case. If the model can make a prediction, it returns the following information: An updated sentiment label and corresponding sentiment level. A confidence level for the prediction. The system: Updates the Current sentiment field with the current sentiment. Compares the updated current sentiment to the original current sentiment, calculates the change in sentiment, and updates the Sentiment over time field. If there is an increase in the score, the Sentiment over time field shows Improving. If there is a decrease in the score, the Sentiment over time field shows Declining. If there is no change in the score, the Sentiment over time field continues to display the previous value. Note: If the Original sentiment is Neutral and the Current sentiment is Neutral, then the Sentiment over time is Neutral. If the model can't make a prediction, no information gets recorded and the value in the Current sentiment field remains the same.

For more information about the pre-trained machine learning model, see Create a model to predict case sentiment.

Prediction feedback

The system stores feedback on prediction results in the Predictor Result [ml_predictor_results] table. Users with the ml_admin role can access the table and view the results. For sentiment analysis:

The default value in the Predicted correctly field for each sentiment prediction is set to true.
The Final input value and Final output value fields remain empty because sentiment analysis predictions do not collect feedback from agents.

The Predictor Result table also stores information about skipped and failed predictions. For more information about this table, see Components installed with Task Intelligence for Customer Service.