Analyzing NICE Cognigy AI Sentiment Scores via REST API with Go

What You Will Build

• A Go service that submits conversation text segments to the Cognigy AI sentiment analysis API, validates payloads against token limits and model availability, processes asynchronous job results, applies threshold calibration for emotion classification, synchronizes results via webhooks, tracks MLOps latency metrics, and generates governance audit logs.
• This implementation uses the Cognigy Cloud REST API surface (/api/v1/sentiment/analyze, /api/v1/sentiment/jobs/{id}, /api/v1/ai/models/available) and standard Go HTTP client patterns.
• The tutorial covers Go 1.21+ with net/http, golang.org/x/oauth2, and encoding/json.

Prerequisites

• OAuth client credentials with scopes: ai.sentiment.analyze, analytics.read, ai.models.read
• Cognigy Cloud tenant URL (e.g., https://your-tenant.cognigy.ai)
• Go 1.21 or later
• External dependencies: golang.org/x/oauth2 (installed via go get golang.org/x/oauth2)
• A valid webhook receiver endpoint for result synchronization

Authentication Setup

Cognigy uses OAuth 2.0 Client Credentials flow. The authentication request must target the tenant-specific token endpoint and request the exact scopes required for sentiment analysis and model discovery.

package main

import (
	"context"
	"fmt"
	"net/http"
	"time"

	"golang.org/x/oauth2/clientcredentials"
)

func getToken(clientID, clientSecret, tenantBaseURL string) (string, error) {
	conf := &clientcredentials.Config{
		ClientID:     clientID,
		ClientSecret: clientSecret,
		TokenURL:     fmt.Sprintf("%s/oauth/token", tenantBaseURL),
		Scopes:       []string{"ai.sentiment.analyze", "analytics.read", "ai.models.read"},
	}

	client := conf.Client(context.Background())
	client.Timeout = 10 * time.Second

	// Trigger token fetch
	_, err := client.Get(fmt.Sprintf("%s/api/v1/health", tenantBaseURL))
	if err != nil {
		return "", fmt.Errorf("oauth token exchange failed: %w", err)
	}

	token, err := conf.Token(context.Background())
	if err != nil {
		return "", fmt.Errorf("token retrieval failed: %w", err)
	}

	return token.AccessToken, nil
}

The clientcredentials.Config handles token caching automatically. The health check triggers the initial token exchange. Subsequent calls reuse the cached token until expiration. The required scope ai.sentiment.analyze is mandatory for job submission. ai.models.read enables model availability validation.

Implementation

Step 1: Construct Analysis Payloads with Text Segments and Directives

The Cognigy sentiment API accepts structured payloads containing text segments, language model references, and granularity directives. Each segment must carry a unique identifier for result mapping.

type SentimentRequest struct {
	Segments      []Segment      `json:"segments"`
	LanguageModel string         `json:"languageModel"`
	Granularity   string         `json:"granularity"`
	Normalization bool           `json:"normalization"`
}

type Segment struct {
	ID   string `json:"id"`
	Text string `json:"text"`
}

func buildAnalysisPayload(segments []Segment, model string, granularity string) *SentimentRequest {
	return &SentimentRequest{
		Segments:      segments,
		LanguageModel: model,
		Granularity:   granularity,
		Normalization: true,
	}
}

The granularity field accepts segment, sentence, or word. Setting normalization to true triggers automatic score normalization across languages, ensuring consistent output ranges between -1.0 and 1.0. The languageModel field references a deployed model identifier such as en_us_sentiment_v2 or de_de_emotion_v1.

Step 2: Validate Schemas Against Token Limits and Model Availability

Before submission, validate segment token counts and verify model availability. Cognigy enforces a 1024 token limit per request and restricts inference to active models.

import (
	"encoding/json"
	"fmt"
	"net/http"
	"strings"
)

type ModelAvailability struct {
	ModelID   string `json:"modelId"`
	Status    string `json:"status"`
	MaxTokens int    `json:"maxTokens"`
}

func validatePayload(req *SentimentRequest, token string, baseURL string) error {
	// Fetch available models
	modelResp, err := http.Get(fmt.Sprintf("%s/api/v1/ai/models/available", baseURL))
	if err != nil {
		return fmt.Errorf("model availability check failed: %w", err)
	}
	defer modelResp.Body.Close()

	if modelResp.StatusCode != http.StatusOK {
		return fmt.Errorf("model endpoint returned %d", modelResp.StatusCode)
	}

	var models []ModelAvailability
	if err := json.NewDecoder(modelResp.Body).Decode(&models); err != nil {
		return fmt.Errorf("model payload decode failed: %w", err)
	}

	// Verify requested model exists and is active
	modelActive := false
	var targetModel ModelAvailability
	for _, m := range models {
		if m.ModelID == req.LanguageModel {
			modelActive = true
			targetModel = m
			break
		}
	}
	if !modelActive {
		return fmt.Errorf("model %s is not available or inactive", req.LanguageModel)
	}

	// Validate token limits per segment (approximate: 1 word ~= 1.3 tokens)
	for _, seg := range req.Segments {
		words := len(strings.Fields(seg.Text))
		estimatedTokens := float64(words) * 1.3
		if estimatedTokens > float64(targetModel.MaxTokens) {
			return fmt.Errorf("segment %s exceeds token limit (%.0f > %d)", seg.ID, estimatedTokens, targetModel.MaxTokens)
		}
	}

	return nil
}

The validation function queries /api/v1/ai/models/available to retrieve the current model matrix. It cross-references the requested languageModel against active deployments. Token estimation uses a standard 1.3 multiplier for English text. Adjust the multiplier for high-morphology languages. The function returns early on mismatch to prevent inference failures.

Step 3: Handle Asynchronous Job Processing with Retry Logic

Sentiment analysis runs asynchronously. Submit the payload to /api/v1/sentiment/analyze, capture the job identifier, and poll /api/v1/sentiment/jobs/{id} until completion. Implement exponential backoff for 429 rate limits.

import (
	"bytes"
	"context"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
	"time"
)

type JobResponse struct {
	JobID   string `json:"jobId"`
	Status  string `json:"status"`
	Message string `json:"message"`
}

type SentimentResult struct {
	SegmentID string  `json:"segmentId"`
	Score     float64 `json:"score"`
	Emotions  map[string]float64 `json:"emotions"`
	Confidence float64 `json:"confidence"`
}

func submitJob(req *SentimentRequest, token string, baseURL string) (string, error) {
	payload, err := json.Marshal(req)
	if err != nil {
		return "", fmt.Errorf("payload marshal failed: %w", err)
	}

	httpReq, err := http.NewRequestWithContext(context.Background(), http.MethodPost,
		fmt.Sprintf("%s/api/v1/sentiment/analyze", baseURL), bytes.NewBuffer(payload))
	if err != nil {
		return "", fmt.Errorf("request creation failed: %w", err)
	}

	httpReq.Header.Set("Authorization", "Bearer "+token)
	httpReq.Header.Set("Content-Type", "application/json")

	client := &http.Client{Timeout: 30 * time.Second}
	resp, err := client.Do(httpReq)
	if err != nil {
		return "", fmt.Errorf("job submission failed: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode == http.StatusTooManyRequests {
		return "", fmt.Errorf("rate limited (429): backoff required")
	}
	if resp.StatusCode != http.StatusAccepted && resp.StatusCode != http.StatusOK {
		body, _ := io.ReadAll(resp.Body)
		return "", fmt.Errorf("submission failed %d: %s", resp.StatusCode, string(body))
	}

	var jobResp JobResponse
	if err := json.NewDecoder(resp.Body).Decode(&jobResp); err != nil {
		return "", fmt.Errorf("job response decode failed: %w", err)
	}

	return jobResp.JobID, nil
}

func pollJob(jobID string, token string, baseURL string) ([]SentimentResult, error) {
	client := &http.Client{Timeout: 30 * time.Second}
	maxRetries := 10
	backoff := 1 * time.Second

	for i := 0; i < maxRetries; i++ {
		time.Sleep(backoff)
		backoff *= 2

		httpReq, err := http.NewRequestWithContext(context.Background(), http.MethodGet,
			fmt.Sprintf("%s/api/v1/sentiment/jobs/%s", baseURL, jobID), nil)
		if err != nil {
			return nil, fmt.Errorf("poll request failed: %w", err)
		}

		httpReq.Header.Set("Authorization", "Bearer "+token)

		resp, err := client.Do(httpReq)
		if err != nil {
			return nil, fmt.Errorf("poll execution failed: %w", err)
		}
		defer resp.Body.Close()

		if resp.StatusCode == http.StatusTooManyRequests {
			continue
		}

		if resp.StatusCode == http.StatusNotFound {
			return nil, fmt.Errorf("job %s not found", jobID)
		}

		body, err := io.ReadAll(resp.Body)
		if err != nil {
			return nil, fmt.Errorf("response read failed: %w", err)
		}

		var jobStatus JobResponse
		if err := json.Unmarshal(body, &jobStatus); err == nil && jobStatus.Status == "completed" {
			// Extract results from completed payload
			var results []SentimentResult
			if err := json.Unmarshal(body, &results); err != nil {
				// Fallback: Cognigy sometimes wraps results in a "data" field
				var wrapped struct {
					Data []SentimentResult `json:"data"`
				}
				if wErr := json.Unmarshal(body, &wrapped); wErr == nil {
					return wrapped.Data, nil
				}
			}
			return results, nil
		}

		if resp.StatusCode != http.StatusOK {
			return nil, fmt.Errorf("poll failed %d: %s", resp.StatusCode, string(body))
		}
	}

	return nil, fmt.Errorf("job %s did not complete within timeout", jobID)
}

The submission function handles 429 responses explicitly. The polling function implements exponential backoff starting at 1 second, doubling each iteration. It decodes the final response, accounting for Cognigy’s occasional payload wrapping. The function returns a slice of SentimentResult containing normalized scores and emotion breakdowns.

Step 4: Implement Sentiment Tuning Logic with Threshold Calibration

Raw sentiment scores require threshold calibration to reduce false positives during bot interaction monitoring. Map continuous scores to discrete categories and apply emotion classification pipelines.

type CalibrationConfig struct {
	NegativeThreshold float64 `json:"negativeThreshold"`
	PositiveThreshold float64 `json:"positiveThreshold"`
	EmotionWeight     float64 `json:"emotionWeight"`
}

type ClassifiedResult struct {
	SegmentID    string  `json:"segmentId"`
	RawScore     float64 `json:"rawScore"`
	Category     string  `json:"category"`
	PrimaryEmotion string `json:"primaryEmotion"`
	Confidence   float64 `json:"confidence"`
}

func applyThresholdCalibration(results []SentimentResult, config CalibrationConfig) []ClassifiedResult {
	classified := make([]ClassifiedResult, 0, len(results))

	for _, r := range results {
		category := "neutral"
		if r.Score <= config.NegativeThreshold {
			category = "negative"
		} else if r.Score >= config.PositiveThreshold {
			category = "positive"
		}

		// Emotion classification pipeline
		primaryEmotion := "none"
		maxEmotionScore := 0.0
		for emotion, score := range r.Emotions {
			if score > maxEmotionScore {
				maxEmotionScore = score
				primaryEmotion = emotion
			}
		}

		// Apply emotion weight to confidence
		adjustedConfidence := r.Confidence * (1.0 + config.EmotionWeight)
		if adjustedConfidence > 1.0 {
			adjustedConfidence = 1.0
		}

		classified = append(classified, ClassifiedResult{
			SegmentID:      r.SegmentID,
			RawScore:       r.Score,
			Category:       category,
			PrimaryEmotion: primaryEmotion,
			Confidence:     adjustedConfidence,
		})
	}

	return classified
}

The calibration function accepts configurable thresholds. Default values typically set NegativeThreshold to -0.3 and PositiveThreshold to 0.3. The emotion classification pipeline iterates through the Emotions map to identify the dominant signal. The EmotionWeight parameter adjusts confidence scoring to account for high-arousal states like frustration or urgency.

Step 5: Synchronize Results, Track Latency, and Generate Audit Logs

Finalize the pipeline by pushing classified results to external analytics platforms via webhook, recording evaluation latency for MLOps efficiency, and writing structured audit logs for governance compliance.

import (
	"crypto/sha256"
	"encoding/hex"
	"encoding/json"
	"fmt"
	"net/http"
	"os"
	"time"
)

type AuditLog struct {
	Timestamp   string `json:"timestamp"`
	JobID       string `json:"jobId"`
	InputHash   string `json:"inputHash"`
	RecordCount int    `json:"recordCount"`
	LatencyMs   int64  `json:"latencyMs"`
	Status      string `json:"status"`
}

type WebhookPayload struct {
	TenantID string            `json:"tenantId"`
	Timestamp string          `json:"timestamp"`
	Results  []ClassifiedResult `json:"results"`
	Metrics  map[string]float64 `json:"metrics"`
}

func generateAuditLog(jobID string, inputPayload []byte, latency time.Duration, status string) string {
	hash := sha256.Sum256(inputPayload)
	log := AuditLog{
		Timestamp:   time.Now().UTC().Format(time.RFC3339),
		JobID:       jobID,
		InputHash:   hex.EncodeToString(hash[:]),
		RecordCount: 0,
		LatencyMs:   latency.Milliseconds(),
		Status:      status,
	}

	logBytes, _ := json.Marshal(log)
	return string(logBytes)
}

func syncToWebhook(payload WebhookPayload, webhookURL string) error {
	body, err := json.Marshal(payload)
	if err != nil {
		return fmt.Errorf("webhook payload marshal failed: %w", err)
	}

	req, err := http.NewRequestWithContext(context.Background(), http.MethodPost, webhookURL, bytes.NewBuffer(body))
	if err != nil {
		return fmt.Errorf("webhook request creation failed: %w", err)
	}

	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("X-Sentiment-Source", "cognigy-ai-go")

	client := &http.Client{Timeout: 15 * time.Second}
	resp, err := client.Do(req)
	if err != nil {
		return fmt.Errorf("webhook delivery failed: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode >= 400 {
		return fmt.Errorf("webhook returned %d", resp.StatusCode)
	}

	return nil
}

func writeAuditLog(logEntry string) error {
	f, err := os.OpenFile("sentiment_audit.log", os.O_APPEND|os.O_CREATE|os.O_WRONLY, 0644)
	if err != nil {
		return fmt.Errorf("audit log file open failed: %w", err)
	}
	defer f.Close()

	_, err = f.WriteString(logEntry + "\n")
	return err
}

The audit log function computes a SHA-256 hash of the original input payload to ensure data integrity tracking. The webhook synchronization function includes a custom header for source identification. Latency tracking occurs at the pipeline level, capturing the duration from payload submission to result classification.

Complete Working Example

The following module integrates all components into a runnable Go service. Replace placeholder credentials and URLs before execution.

package main

import (
	"bytes"
	"context"
	"encoding/json"
	"fmt"
	"net/http"
	"os"
	"time"

	"golang.org/x/oauth2/clientcredentials"
)

// Models and payloads defined in previous steps
type SentimentRequest struct {
	Segments      []Segment      `json:"segments"`
	LanguageModel string         `json:"languageModel"`
	Granularity   string         `json:"granularity"`
	Normalization bool           `json:"normalization"`
}

type Segment struct {
	ID   string `json:"id"`
	Text string `json:"text"`
}

type ModelAvailability struct {
	ModelID   string `json:"modelId"`
	Status    string `json:"status"`
	MaxTokens int    `json:"maxTokens"`
}

type JobResponse struct {
	JobID   string `json:"jobId"`
	Status  string `json:"status"`
	Message string `json:"message"`
}

type SentimentResult struct {
	SegmentID  string            `json:"segmentId"`
	Score      float64           `json:"score"`
	Emotions   map[string]float64 `json:"emotions"`
	Confidence float64           `json:"confidence"`
}

type CalibrationConfig struct {
	NegativeThreshold float64 `json:"negativeThreshold"`
	PositiveThreshold float64 `json:"positiveThreshold"`
	EmotionWeight     float64 `json:"emotionWeight"`
}

type ClassifiedResult struct {
	SegmentID      string  `json:"segmentId"`
	RawScore       float64 `json:"rawScore"`
	Category       string  `json:"category"`
	PrimaryEmotion string  `json:"primaryEmotion"`
	Confidence     float64 `json:"confidence"`
}

type AuditLog struct {
	Timestamp   string `json:"timestamp"`
	JobID       string `json:"jobId"`
	InputHash   string `json:"inputHash"`
	RecordCount int    `json:"recordCount"`
	LatencyMs   int64  `json:"latencyMs"`
	Status      string `json:"status"`
}

type WebhookPayload struct {
	TenantID string             `json:"tenantId"`
	Timestamp string           `json:"timestamp"`
	Results  []ClassifiedResult `json:"results"`
	Metrics  map[string]float64 `json:"metrics"`
}

func main() {
	tenantBaseURL := os.Getenv("COGNIGY_BASE_URL")
	clientID := os.Getenv("COGNIGY_CLIENT_ID")
	clientSecret := os.Getenv("COGNIGY_CLIENT_SECRET")
	webhookURL := os.Getenv("WEBHOOK_URL")

	if tenantBaseURL == "" || clientID == "" || clientSecret == "" {
		fmt.Println("Missing required environment variables")
		os.Exit(1)
	}

	// 1. Authenticate
	token, err := getToken(clientID, clientSecret, tenantBaseURL)
	if err != nil {
		fmt.Printf("Authentication failed: %v\n", err)
		os.Exit(1)
	}

	// 2. Construct payload
	segments := []Segment{
		{ID: "seg_001", Text: "I have been waiting for over an hour and nobody has helped me."},
		{ID: "seg_002", Text: "The agent resolved my billing issue quickly and politely."},
	}
	req := buildAnalysisPayload(segments, "en_us_sentiment_v2", "segment")

	// 3. Validate
	if err := validatePayload(req, token, tenantBaseURL); err != nil {
		fmt.Printf("Validation failed: %v\n", err)
		os.Exit(1)
	}

	inputBytes, _ := json.Marshal(req)

	// 4. Submit and poll
	startTime := time.Now()
	jobID, err := submitJob(req, token, tenantBaseURL)
	if err != nil {
		fmt.Printf("Job submission failed: %v\n", err)
		os.Exit(1)
	}

	results, err := pollJob(jobID, token, tenantBaseURL)
	if err != nil {
		fmt.Printf("Job polling failed: %v\n", err)
		os.Exit(1)
	}

	latency := time.Since(startTime)

	// 5. Calibrate
	config := CalibrationConfig{
		NegativeThreshold: -0.3,
		PositiveThreshold: 0.3,
		EmotionWeight:     0.1,
	}
	classified := applyThresholdCalibration(results, config)

	// 6. Audit log
	auditEntry := generateAuditLog(jobID, inputBytes, latency, "completed")
	if err := writeAuditLog(auditEntry); err != nil {
		fmt.Printf("Audit write failed: %v\n", err)
	}

	// 7. Webhook sync
	webhookPayload := WebhookPayload{
		TenantID:  "demo-tenant",
		Timestamp: time.Now().UTC().Format(time.RFC3339),
		Results:   classified,
		Metrics: map[string]float64{
			"latency_ms": float64(latency.Milliseconds()),
			"records":    float64(len(classified)),
		},
	}

	if err := syncToWebhook(webhookPayload, webhookURL); err != nil {
		fmt.Printf("Webhook sync failed: %v\n", err)
	}

	fmt.Printf("Pipeline completed. Latency: %v. Records: %d\n", latency, len(classified))
}

// Helper functions from previous steps included here for completeness
func getToken(clientID, clientSecret, tenantBaseURL string) (string, error) {
	conf := &clientcredentials.Config{
		ClientID:     clientID,
		ClientSecret: clientSecret,
		TokenURL:     fmt.Sprintf("%s/oauth/token", tenantBaseURL),
		Scopes:       []string{"ai.sentiment.analyze", "analytics.read", "ai.models.read"},
	}
	client := conf.Client(context.Background())
	client.Timeout = 10 * time.Second
	_, err := client.Get(fmt.Sprintf("%s/api/v1/health", tenantBaseURL))
	if err != nil {
		return "", fmt.Errorf("oauth token exchange failed: %w", err)
	}
	token, err := conf.Token(context.Background())
	if err != nil {
		return "", fmt.Errorf("token retrieval failed: %w", err)
	}
	return token.AccessToken, nil
}

func buildAnalysisPayload(segments []Segment, model string, granularity string) *SentimentRequest {
	return &SentimentRequest{
		Segments:      segments,
		LanguageModel: model,
		Granularity:   granularity,
		Normalization: true,
	}
}

func validatePayload(req *SentimentRequest, token string, baseURL string) error {
	modelResp, err := http.Get(fmt.Sprintf("%s/api/v1/ai/models/available", baseURL))
	if err != nil {
		return fmt.Errorf("model availability check failed: %w", err)
	}
	defer modelResp.Body.Close()
	if modelResp.StatusCode != http.StatusOK {
		return fmt.Errorf("model endpoint returned %d", modelResp.StatusCode)
	}
	var models []ModelAvailability
	if err := json.NewDecoder(modelResp.Body).Decode(&models); err != nil {
		return fmt.Errorf("model payload decode failed: %w", err)
	}
	modelActive := false
	var targetModel ModelAvailability
	for _, m := range models {
		if m.ModelID == req.LanguageModel {
			modelActive = true
			targetModel = m
			break
		}
	}
	if !modelActive {
		return fmt.Errorf("model %s is not available or inactive", req.LanguageModel)
	}
	for _, seg := range req.Segments {
		words := len(strings.Fields(seg.Text))
		estimatedTokens := float64(words) * 1.3
		if estimatedTokens > float64(targetModel.MaxTokens) {
			return fmt.Errorf("segment %s exceeds token limit (%.0f > %d)", seg.ID, estimatedTokens, targetModel.MaxTokens)
		}
	}
	return nil
}

func submitJob(req *SentimentRequest, token string, baseURL string) (string, error) {
	payload, err := json.Marshal(req)
	if err != nil {
		return "", fmt.Errorf("payload marshal failed: %w", err)
	}
	httpReq, err := http.NewRequestWithContext(context.Background(), http.MethodPost,
		fmt.Sprintf("%s/api/v1/sentiment/analyze", baseURL), bytes.NewBuffer(payload))
	if err != nil {
		return "", fmt.Errorf("request creation failed: %w", err)
	}
	httpReq.Header.Set("Authorization", "Bearer "+token)
	httpReq.Header.Set("Content-Type", "application/json")
	client := &http.Client{Timeout: 30 * time.Second}
	resp, err := client.Do(httpReq)
	if err != nil {
		return "", fmt.Errorf("job submission failed: %w", err)
	}
	defer resp.Body.Close()
	if resp.StatusCode == http.StatusTooManyRequests {
		return "", fmt.Errorf("rate limited (429): backoff required")
	}
	if resp.StatusCode != http.StatusAccepted && resp.StatusCode != http.StatusOK {
		body, _ := io.ReadAll(resp.Body)
		return "", fmt.Errorf("submission failed %d: %s", resp.StatusCode, string(body))
	}
	var jobResp JobResponse
	if err := json.NewDecoder(resp.Body).Decode(&jobResp); err != nil {
		return "", fmt.Errorf("job response decode failed: %w", err)
	}
	return jobResp.JobID, nil
}

func pollJob(jobID string, token string, baseURL string) ([]SentimentResult, error) {
	client := &http.Client{Timeout: 30 * time.Second}
	maxRetries := 10
	backoff := 1 * time.Second
	for i := 0; i < maxRetries; i++ {
		time.Sleep(backoff)
		backoff *= 2
		httpReq, err := http.NewRequestWithContext(context.Background(), http.MethodGet,
			fmt.Sprintf("%s/api/v1/sentiment/jobs/%s", baseURL, jobID), nil)
		if err != nil {
			return nil, fmt.Errorf("poll request failed: %w", err)
		}
		httpReq.Header.Set("Authorization", "Bearer "+token)
		resp, err := client.Do(httpReq)
		if err != nil {
			return nil, fmt.Errorf("poll execution failed: %w", err)
		}
		defer resp.Body.Close()
		if resp.StatusCode == http.StatusTooManyRequests {
			continue
		}
		if resp.StatusCode == http.StatusNotFound {
			return nil, fmt.Errorf("job %s not found", jobID)
		}
		body, err := io.ReadAll(resp.Body)
		if err != nil {
			return nil, fmt.Errorf("response read failed: %w", err)
		}
		var jobStatus JobResponse
		if err := json.Unmarshal(body, &jobStatus); err == nil && jobStatus.Status == "completed" {
			var results []SentimentResult
			if err := json.Unmarshal(body, &results); err != nil {
				var wrapped struct {
					Data []SentimentResult `json:"data"`
				}
				if wErr := json.Unmarshal(body, &wrapped); wErr == nil {
					return wrapped.Data, nil
				}
			}
			return results, nil
		}
		if resp.StatusCode != http.StatusOK {
			return nil, fmt.Errorf("poll failed %d: %s", resp.StatusCode, string(body))
		}
	}
	return nil, fmt.Errorf("job %s did not complete within timeout", jobID)
}

func applyThresholdCalibration(results []SentimentResult, config CalibrationConfig) []ClassifiedResult {
	classified := make([]ClassifiedResult, 0, len(results))
	for _, r := range results {
		category := "neutral"
		if r.Score <= config.NegativeThreshold {
			category = "negative"
		} else if r.Score >= config.PositiveThreshold {
			category = "positive"
		}
		primaryEmotion := "none"
		maxEmotionScore := 0.0
		for emotion, score := range r.Emotions {
			if score > maxEmotionScore {
				maxEmotionScore = score
				primaryEmotion = emotion
			}
		}
		adjustedConfidence := r.Confidence * (1.0 + config.EmotionWeight)
		if adjustedConfidence > 1.0 {
			adjustedConfidence = 1.0
		}
		classified = append(classified, ClassifiedResult{
			SegmentID:      r.SegmentID,
			RawScore:       r.Score,
			Category:       category,
			PrimaryEmotion: primaryEmotion,
			Confidence:     adjustedConfidence,
		})
	}
	return classified
}

func generateAuditLog(jobID string, inputPayload []byte, latency time.Duration, status string) string {
	hash := sha256.Sum256(inputPayload)
	log := AuditLog{
		Timestamp:   time.Now().UTC().Format(time.RFC3339),
		JobID:       jobID,
		InputHash:   hex.EncodeToString(hash[:]),
		RecordCount: 0,
		LatencyMs:   latency.Milliseconds(),
		Status:      status,
	}
	logBytes, _ := json.Marshal(log)
	return string(logBytes)
}

func syncToWebhook(payload WebhookPayload, webhookURL string) error {
	body, err := json.Marshal(payload)
	if err != nil {
		return fmt.Errorf("webhook payload marshal failed: %w", err)
	}
	req, err := http.NewRequestWithContext(context.Background(), http.MethodPost, webhookURL, bytes.NewBuffer(body))
	if err != nil {
		return fmt.Errorf("webhook request creation failed: %w", err)
	}
	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("X-Sentiment-Source", "cognigy-ai-go")
	client := &http.Client{Timeout: 15 * time.Second}
	resp, err := client.Do(req)
	if err != nil {
		return fmt.Errorf("webhook delivery failed: %w", err)
	}
	defer resp.Body.Close()
	if resp.StatusCode >= 400 {
		return fmt.Errorf("webhook returned %d", resp.StatusCode)
	}
	return nil
}

func writeAuditLog(logEntry string) error {
	f, err := os.OpenFile("sentiment_audit.log", os.O_APPEND|os.O_CREATE|os.O_WRONLY, 0644)
	if err != nil {
		return fmt.Errorf("audit log file open failed: %w", err)
	}
	defer f.Close()
	_, err = f.WriteString(logEntry + "\n")
	return err
}

Add import "crypto/sha256", import "encoding/hex", import "io", and import "strings" to the complete example for full compilation. Execute with go run main.go after setting environment variables.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token or missing ai.sentiment.analyze scope.
• Fix: Verify client credentials and scope configuration. Implement token refresh logic by re-calling getToken when 401 responses occur. Cache tokens with a TTL buffer of 60 seconds before expiration.

Error: 403 Forbidden

• Cause: Tenant permissions restrict AI model access or the webhook receiver blocks external POST requests.
• Fix: Confirm the OAuth client has ai.sentiment.analyze and ai.models.read scopes assigned in the Cognigy administration console. Verify webhook receiver allows POST requests and returns 2xx status codes.

Error: 429 Too Many Requests

• Cause: Exceeding Cognigy API rate limits during job polling or submission.
• Fix: The polling function implements exponential backoff. Add jitter to production deployments to prevent thundering herd scenarios. Monitor Retry-After headers and adjust sleep duration accordingly.

Error: 400 Bad Request

• Cause: Payload exceeds token limits, invalid granularity value, or unsupported language model reference.
• Fix: Review validation output. Ensure granularity matches segment, sentence, or word. Verify languageModel against the /api/v1/ai/models/available response. Truncate or split segments exceeding MaxTokens.

Error: 500 Internal Server Error

• Cause: Transient inference engine failure or model deployment mismatch.
• Fix: Implement circuit breaker patterns for repeated 5xx responses. Retry submission after 5 seconds. Check Cognigy status dashboards for AI service outages. Log job IDs for support ticket references.

Official References

• Cognigy Cloud API Documentation
• NICE CXone Analytics API Reference
• OAuth 2.0 Client Credentials Flow Specification
• Go net/http Package Documentation