--- sourceDocument: Brazil Customer Service Management sourceDocumentLink: https://www.servicenow.com/docs/r/customer-service-management Release : - brazil ft:locale : - en-US ft:publication_title : - Brazil Customer Service Management ft:clusterId : - csm bundleId : - csm workflow : - Customer and Industry --- # Sentiment Analysis # Sentiment Analysis {#ariaid-title1} Release version: Brazil Updated September 10, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of Sentiment Analysis Sentiment Analysis in ServiceNow's Task Intelligence for Customer Service helps you understand customer emotions by analyzing email and case text. This insight enables you to deliver more empathetic, responsive support experiences by identifying the sentiment of new and updated cases and displaying this information to agents and managers. Show full answer Show less ## Key Features * **Sentiment Evaluation:** Automatically analyzes the subject, body, short description, and comments of cases and emails to determine sentiment when a case is created or updated. * **Sentiment Labels and Scores:** Classifies sentiment as Positive (1.0), Neutral (0.5), or Negative (0.0) along with a confidence score. * **Sentiment Tracking Over Time:** Updates ongoing sentiment with each case update, tracking whether sentiment is Improving, Declining, or Neutral over time. * **Agent and Manager Use Cases:** Helps agents prioritize work based on current sentiment and trends. Assists managers in routing cases to agents with empathy skills, monitoring cases, reassigning as needed, avoiding escalations, and identifying coaching opportunities from negative sentiment cases. * **Language Support:** In the Brazil release, sentiment analysis supports prediction for cases created in English. * **Machine Learning Model:** Utilizes a pre-trained model to perform sentiment classification automatically upon case creation and updates. * **Prediction Feedback and Monitoring:** Sentiment prediction results and confidence levels are stored in the Predictor Results for Task table. Users with the mladmin role can review prediction accuracy, although agent feedback on sentiment predictions is not collected. ## Practical Benefits for ServiceNow Customers By leveraging sentiment analysis, you can enhance customer service quality through real-time emotional insight, enabling your team to respond more effectively to customer needs. Prioritizing cases based on sentiment helps optimize workload management, while continuous sentiment tracking supports proactive case management and coaching. This ultimately leads to improved customer satisfaction and reduced escalation rates. 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. {#case-sentiment-analysis__ul_cwf_gdq_f5b} 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 Brazil release, the sentiment analysis feature can predict sentiment for cases created in English. ## Sentiment analysis machine learning models {#case-sentiment-analysis__section_ep1_mmf_c5b} 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.{#case-sentiment-analysis__table_akj_3ms_jyb__entry__2} | 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. {#case-sentiment-analysis__ul_rxj_jms_jyb} 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) {#case-sentiment-analysis__ul_txj_jms_jyb} * A confidence level for the prediction. {#case-sentiment-analysis__ul_sxj_jms_jyb} 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. {#case-sentiment-analysis__ul_htd_vts_jyb} 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. {#case-sentiment-analysis__ul_itd_vts_jyb} 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. {#case-sentiment-analysis__ul_ktd_vts_jyb} Note: If the Original sentiment is Neutral and the Current sentiment is Neutral, then the Sentiment over time is Neutral. {#case-sentiment-analysis__ul_jtd_vts_jyb} If the model can't make a prediction, no information gets recorded and the value in the Current sentiment field remains the same. | [Table 1. Sentiment analysis for cases] {#case-sentiment-analysis__table_akj_3ms_jyb} For more information about the pre-trained machine learning model, see [Create a model to predict case sentiment](https://www.servicenow.com/docs/CuLYb5nVZ_rlK3yihHVwqQ "Edit and test the pre-trained sentiment model to predict sentiment for customer service cases."). ## Prediction feedback {#case-sentiment-analysis__section_lvk_gdc_yyb} 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. {#case-sentiment-analysis__ul_ulw_dcg_c5b} 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](https://www.servicenow.com/docs/QLP47nbNSCgOX9j2K22X_Q "Several types of components are installed with the Task Intelligence for Customer Service application, including tables, roles, properties, flows, and scheduled jobs."). **Related concepts** * [Configure Sentiment Analysis](https://www.servicenow.com/docs/Cw7T3fxzW1WBPMwiFQnz0A "Activate the required plugin, enable the sentiment analysis property, and assign roles to use the sentiment analysis feature.") * [Configure Sentiment Analysis](https://www.servicenow.com/docs/Cw7T3fxzW1WBPMwiFQnz0A "Task Intelligence Admin Console") * [Create a model to predict case sentiment](https://www.servicenow.com/docs/CuLYb5nVZ_rlK3yihHVwqQ "Edit and test the pre-trained sentiment model to predict sentiment for customer service cases.")