hrsd-sentiment-analysis

name	hrsd-sentiment-analysis
version	1.0.0
description	Analyze employee sentiment from HR cases, surveys, and interactions to track trends, identify flight risk indicators, and flag cases needing manager attention
author	Happy Technologies LLC
tags	["hrsd","sentiment","analysis","employee-experience","flight-risk","surveys","trends"]
platforms	["claude-code","claude-desktop","chatgpt","cursor","any"]
tools	{"mcp":["SN-Query-Table","SN-NL-Search","SN-Get-Record","SN-Add-Work-Notes","SN-Update-Record"],"rest":["/api/now/table/sn_hr_core_case","/api/now/table/sn_hr_core_task","/api/now/table/sn_hr_core_profile","/api/now/table/sys_journal_field","/api/now/table/interaction","/api/now/table/asmt_assessment_instance","/api/now/table/asmt_assessment_instance_question","/api/now/table/survey_response","/api/now/table/sn_hr_le_case_type","/api/now/table/hr_category"],"native":["Bash"]}
complexity	advanced
estimated_time	10-25 minutes

Employee Sentiment Analysis

Overview

This skill analyzes employee sentiment across HR Service Delivery touchpoints including cases, surveys, chat interactions, and feedback. It helps you:

• Assess sentiment polarity (positive, neutral, negative) from HR case communications
• Aggregate survey responses and satisfaction scores for trend analysis
• Identify flight risk indicators based on case patterns and sentiment decline
• Flag cases with escalating negativity for proactive manager outreach
• Track department-level and organization-wide sentiment trends over time
• Correlate sentiment signals with employee lifecycle events (tenure, role changes, reviews)

When to use: When HR leaders need visibility into employee satisfaction trends, when agents need to prioritize cases with negative sentiment, or when identifying employees at risk of attrition.

Prerequisites

• Roles: sn_hr_core.manager, sn_hr_core.case_reader, or sn_hr_core.admin
• Plugins: com.sn_hr_service_delivery (HR Service Delivery), com.glide.assessment (Assessment/Survey)
• Access: Read access to sn_hr_core_case, sn_hr_core_profile, sys_journal_field, interaction, asmt_assessment_instance, and survey_response
• Knowledge: Understanding of your organization's survey cadence, HR case types, and attrition indicators

Procedure

Step 1: Retrieve HR Cases for Sentiment Analysis

Fetch recent HR cases for the target scope (individual, department, or organization).

Using MCP (Individual Employee):

Tool: SN-Query-Table
Parameters:
  table_name: sn_hr_core_case
  query: subject_person=[employee_sys_id]^ORDERBYDESCopened_at
  fields: sys_id,number,short_description,description,state,priority,hr_service,opened_at,closed_at,contact_type,resolution_code,satisfaction_rating,reopened,reopen_count
  limit: 20

Using MCP (Department-wide):

Tool: SN-Query-Table
Parameters:
  table_name: sn_hr_core_case
  query: subject_person.department=[department_sys_id]^opened_at>=javascript:gs.beginningOfLast90Days()^ORDERBYDESCopened_at
  fields: sys_id,number,short_description,state,priority,hr_service,opened_at,subject_person,satisfaction_rating,contact_type
  limit: 100

Using REST API:

GET /api/now/table/sn_hr_core_case?sysparm_query=subject_person.department=[department_sys_id]^opened_at>=javascript:gs.beginningOfLast90Days()^ORDERBYDESCopened_at&sysparm_fields=sys_id,number,short_description,state,priority,hr_service,opened_at,subject_person,satisfaction_rating,contact_type&sysparm_display_value=true&sysparm_limit=100

Step 2: Extract Case Communications for Sentiment Scoring

Pull work notes, comments, and additional details from cases to analyze language and tone.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sys_journal_field
  query: element_id=[case_sys_id]^element=comments^ORelement=work_notes^ORDERBYsys_created_on
  fields: sys_id,element,value,sys_created_on,sys_created_by,element_id
  limit: 50

Using REST API:

GET /api/now/table/sys_journal_field?sysparm_query=element_id=[case_sys_id]^element=comments^ORelement=work_notes^ORDERBYsys_created_on&sysparm_fields=sys_id,element,value,sys_created_on,sys_created_by,element_id&sysparm_limit=50

Step 3: Retrieve Chat and Interaction History

Fetch interaction records to analyze sentiment from live conversations.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: interaction
  query: opened_for=[employee_sys_id]^ORDERBYDESCopened_at
  fields: sys_id,number,type,channel,state,opened_at,closed_at,short_description,wrap_up_comment,satisfaction,direction
  limit: 25

Using REST API:

GET /api/now/table/interaction?sysparm_query=opened_for=[employee_sys_id]^ORDERBYDESCopened_at&sysparm_fields=sys_id,number,type,channel,state,opened_at,closed_at,short_description,wrap_up_comment,satisfaction,direction&sysparm_display_value=true&sysparm_limit=25

Step 4: Retrieve Survey and Assessment Responses

Pull employee survey responses for structured satisfaction data.

Using MCP (Assessment Instances):

Tool: SN-Query-Table
Parameters:
  table_name: asmt_assessment_instance
  query: user=[employee_sys_id]^metric_type.nameLIKEemployee^ORmetric_type.nameLIKEHR^ORDERBYDESCtaken_on
  fields: sys_id,metric_type,taken_on,percent,state,user,category_scores
  limit: 10

Using MCP (Individual Question Responses):

Tool: SN-Query-Table
Parameters:
  table_name: asmt_assessment_instance_question
  query: instance.user=[employee_sys_id]^instance.metric_type.nameLIKEemployee
  fields: sys_id,instance,metric,value,string_value,actual_value,category
  limit: 50

Using REST API:

GET /api/now/table/asmt_assessment_instance?sysparm_query=user=[employee_sys_id]^metric_type.nameLIKEemployee^ORDERBYDESCtaken_on&sysparm_fields=sys_id,metric_type,taken_on,percent,state,user,category_scores&sysparm_display_value=true&sysparm_limit=10

Step 5: Fetch Employee Profile and Lifecycle Context

Retrieve the employee's HR profile to correlate sentiment with lifecycle events.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sn_hr_core_profile
  query: user=[employee_sys_id]
  fields: sys_id,user,department,location,employment_type,hire_date,manager,job_title,cost_center,last_review_date,last_promotion_date,years_in_role,employee_type
  limit: 1

Using REST API:

GET /api/now/table/sn_hr_core_profile?sysparm_query=user=[employee_sys_id]&sysparm_fields=sys_id,user,department,location,employment_type,hire_date,manager,job_title,cost_center,last_review_date,last_promotion_date,years_in_role&sysparm_display_value=true&sysparm_limit=1

Step 6: Analyze Case Type Patterns

Examine the types of cases filed to identify concerning patterns.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sn_hr_core_case
  query: subject_person=[employee_sys_id]^opened_at>=javascript:gs.beginningOfLast12Months()
  fields: sys_id,hr_service,hr_service.name,opened_at,state,priority
  limit: 50

Map case types to sentiment-relevant categories:

=== CASE TYPE PATTERN ANALYSIS ===

High-Risk Case Types (negative sentiment indicators):
- Workplace Complaint / Grievance
- Accommodation Request (especially repeated)
- Policy Dispute / Exception Request
- Manager Escalation
- Exit / Separation Inquiry

Neutral Case Types:
- Benefits Enrollment / Change
- Address / Personal Info Update
- Payroll Inquiry
- General HR Question

Positive Case Types:
- Internal Mobility / Transfer Request
- Training / Development Request
- Recognition Nomination
- Promotion Processing

Step 7: Compile Sentiment Assessment

Assemble findings into a structured sentiment report:

=== EMPLOYEE SENTIMENT ASSESSMENT ===

Employee: Jane Smith | Engineering | Senior Software Engineer
Tenure: 4.8 years | Last Promotion: 18 months ago
Manager: Bob Johnson

--- Overall Sentiment Score ---
Composite Score: 42/100 (Declining - was 68/100 six months ago)
Trend: Negative trajectory over 3 months
Risk Level: ELEVATED

--- Case Sentiment Breakdown ---
Total Cases (12 months): 7
| Period        | Cases | Avg Sentiment | Dominant Tone     |
|---------------|-------|---------------|-------------------|
| Last 30 days  | 3     | Negative      | Frustrated        |
| 30-90 days    | 2     | Neutral       | Matter-of-fact    |
| 90-365 days   | 2     | Positive      | Appreciative      |

--- Survey Scores ---
| Survey                  | Date       | Score | Org Average |
|-------------------------|------------|-------|-------------|
| Q1 Engagement Pulse     | 2026-03-01 | 3.2/5 | 4.1/5       |
| Q4 Annual Engagement    | 2025-12-15 | 3.8/5 | 4.0/5       |
| Q3 Engagement Pulse     | 2025-09-01 | 4.2/5 | 4.1/5       |

--- Flight Risk Indicators ---
[!] Case volume increasing (3 cases in 30 days vs 2 in prior 60)
[!] Engagement survey score dropped 1.0 point in 6 months
[!] Filed policy exception request (potential dissatisfaction)
[!] No promotion in 18 months (above team average of 14 months)
[ ] No internal mobility applications detected
[ ] No exit-related case types filed

--- Sentiment Signals from Communications ---
- Case HRC0012456: "I've raised this issue multiple times..."
  Signal: Repeat frustration, unresolved concern
- Case HRC0012501: "The process seems unnecessarily complicated"
  Signal: Process dissatisfaction
- Case HRC0012523: "I need to understand my options"
  Signal: Ambiguous; may indicate exploration of alternatives

--- Recommended Actions ---
1. IMMEDIATE: Flag for manager 1-on-1 conversation
2. SHORT-TERM: Resolve open cases within SLA to rebuild trust
3. MEDIUM-TERM: Career development conversation recommended
4. TRACKING: Add to monthly sentiment watch list

Step 8: Flag Case for Manager Attention

If sentiment analysis reveals risk, add a work note and flag for review.

Using MCP:

Tool: SN-Add-Work-Notes
Parameters:
  table_name: sn_hr_core_case
  sys_id: [latest_case_sys_id]
  work_notes: "SENTIMENT ALERT: Employee shows declining sentiment trend (score 42/100, down from 68). Flight risk indicators detected: increasing case volume, declining survey scores, no recent career advancement. Recommend manager outreach and career development discussion."

Tool Usage

MCP Tools Reference

Tool	When to Use
SN-Query-Table	Query cases, profiles, surveys, interactions, journal entries
SN-NL-Search	Natural language search for cases with specific sentiment keywords
SN-Get-Record	Retrieve detailed single records for deep-dive analysis
SN-Add-Work-Notes	Document sentiment findings on case records
SN-Update-Record	Update profile risk indicators or sentiment scores

REST API Reference

Endpoint	Method	Purpose
/api/now/table/sn_hr_core_case	GET	HR cases for sentiment extraction
/api/now/table/sys_journal_field	GET	Case communications and comments
/api/now/table/interaction	GET	Chat and phone interaction history
/api/now/table/asmt_assessment_instance	GET	Survey/assessment response data
/api/now/table/asmt_assessment_instance_question	GET	Individual survey question responses
/api/now/table/sn_hr_core_profile	GET	Employee lifecycle context
/api/now/table/sn_hr_le_case_type	GET	Case type classification

Best Practices

• Use multiple signals: Never rely on a single data point; combine case sentiment, survey scores, and interaction tone for accuracy
• Establish baselines: Compare individual sentiment against department and organization averages for meaningful context
• Track trends, not snapshots: A single negative case is less concerning than a sustained declining trend
• Respect privacy: Sentiment data is sensitive; share only with authorized HR personnel and the employee's direct chain
• Avoid bias in scoring: Focus on observable patterns (case frequency, survey scores) rather than subjective interpretation of isolated comments
• Calibrate flight risk thresholds: Work with HR leadership to define what score thresholds trigger specific interventions
• Consider lifecycle context: New hires in their first 90 days may show different patterns than tenured employees; adjust analysis accordingly
• Document methodology: Record the scoring criteria and data sources used so results are reproducible and auditable

Troubleshooting

"Survey responses not found"

Cause: Surveys may use a different assessment type name or may be in a separate survey plugin Solution: Query asmt_metric_type to list available assessment types. Also check survey_response table as an alternative storage location

"Journal entries return too much data"

Cause: High-volume cases may have hundreds of journal entries Solution: Filter by date range using sys_created_on>=javascript:gs.daysAgo(90) and limit to element=comments for employee-facing communications

"Satisfaction rating field is empty"

Cause: Not all case types collect CSAT; it may be captured post-closure Solution: Check sn_hr_core_case for close_notes and check if a separate satisfaction survey is triggered on case closure via asmt_assessment_instance

"Department-level query times out"

Cause: Large departments with many cases over 12 months can exceed query limits Solution: Break the query into monthly chunks or use sysparm_query=subject_person.department=[id]^opened_at>=YYYY-MM-DD^opened_at<=YYYY-MM-DD with pagination

Examples

Example 1: Individual Employee Sentiment Check

Input: "Analyze sentiment for employee Jane Smith over the past 6 months"

Steps:

1. Look up employee sys_id from sn_hr_core_profile
2. Query cases, interactions, and surveys
3. Score sentiment from communications
4. Compile individual sentiment report

Example 2: Department Sentiment Dashboard

Input: "Show sentiment trends for the Engineering department"

Tool: SN-Query-Table
Parameters:
  table_name: sn_hr_core_case
  query: subject_person.department.name=Engineering^opened_at>=javascript:gs.beginningOfLast12Months()
  fields: number,subject_person,hr_service,opened_at,satisfaction_rating,state,priority
  limit: 200

Aggregate by month and case type to produce trend visualization data.

Example 3: Flight Risk Report for HR Leadership

Input: "Identify employees with highest flight risk indicators"

Tool: SN-Query-Table
Parameters:
  table_name: sn_hr_core_case
  query: hr_service.nameLIKEexit^ORhr_service.nameLIKEseparation^ORhr_service.nameLIKEresignation^opened_at>=javascript:gs.daysAgo(90)
  fields: number,subject_person,hr_service,opened_at,state,subject_person.department
  limit: 50

Cross-reference with declining survey scores and increasing case volumes to prioritize the list.

Related Skills

• hrsd/case-summarization - Detailed case context for sentiment-flagged cases
• hrsd/chat-reply-recommendation - Adjust reply tone based on sentiment analysis
• hrsd/persona-assistant - Personalized support for at-risk employees
• reporting/trend-analysis - Visualization of sentiment trends over time
• reporting/executive-dashboard - Executive-level sentiment metrics