Sentiment Analysis Tool Node

The Sentiment Analysis Tool node provides AI Agents with the ability to analyze text sentiment tendency and intensity. Unlike the Sentiment Analysis Action node, the Tool version is called autonomously by AI based on conversation needs.

Use Cases

Typical Applications

Intelligent Customer Service Optimization - AI identifies user emotions, prioritizes negative feedback
Emotional Conversations - AI adjusts reply style and content based on user sentiment
Conversation Routing - AI routes conversations to different processes based on sentiment
Public Opinion Monitoring - AI automatically analyzes sentiment trends in user feedback
Risk Warning - AI identifies angry or frustrated users, intervenes promptly
Satisfaction Assessment - AI automatically assesses customer satisfaction with products or services

Tool vs Action Differences

Feature	Sentiment Analysis Action	Sentiment Analysis Tool
Execution Method	Direct execution	AI calls on demand
Use Case	Fixed sentiment analysis flow	AI Agent analyzes sentiment when needed
Call Timing	Always executes	AI decides when to call
Input Source	Pre-configured	AI extracts text from conversation

Example Comparison:

Action Method (Fixed Flow):
User Input → Sentiment Analysis → Process Based on Sentiment

Tool Method (Intelligent Interaction):
User: "This product is terrible, I want a refund!"
AI: (Identifies need to analyze sentiment) → Calls sentiment analysis tool → Identifies negative → Adjusts reply strategy
User: "Amazing, I'm very satisfied!"
AI: (Identifies need to analyze sentiment) → Calls sentiment analysis tool → Identifies positive → Expresses gratitude

Node Configuration

Basic Settings (Parameters Panel)

Tool Name (toolName)

Unique identifier used by AI Agent to call the tool.

Field Properties:

Required field
Must be unique within the workflow
Does not support expressions
Format requirements:
- Only letters, numbers, underscores, and hyphens allowed
- Must start with a letter
- Cannot duplicate tool names of other tool nodes in the workflow

Configuration Examples:

javascript

// 1. Concise and clear tool name
toolName: "analyzeSentiment"

// 2. Descriptive tool name
toolName: "detectUserEmotion"

// 3. Tool name with prefix
toolName: "sentimentAnalysisTool"

Naming Recommendations:

Use camelCase or underscore: analyzeSentiment or analyze_sentiment
Self-explanatory: Name should clearly express tool functionality
Avoid too long: Recommended within 20 characters
Avoid special characters: Only use letters, numbers, underscores, and hyphens

Important Notes:

AI Agent identifies and calls tools by tool name, not node name
Tool name must be unique in the workflow; duplicate names will automatically get a suffix
English naming is recommended for consistency with AI calls

Tool Description (toolDescription)

Describes the tool's functionality and use cases. AI decides when to call based on description.

Field Properties:

Required field
Supports expressions
Supports multi-line text

Configuration Examples:

javascript

// 1. Clearly describe tool functionality
"Analyze text sentiment tendency and intensity. Input parameters: input (text to analyze). Returns: sentiment (sentiment classification), score (sentiment intensity), label (sentiment label). Use when identifying user emotional states."

// 2. Explain use cases
"Analyze sentiment tendency of user messages.
Input parameters:
- input: User message text
Returns:
- sentiment: Sentiment classification ("positive" | "negative" | "neutral")
- score: Sentiment intensity score (-1 to 1)
- label: Sentiment label (e.g., "positive", "negative", "neutral")

Call analyzeSentiment tool when need to understand user emotions."

// 3. Explain special purpose
"Identify sentiment tendency of customer feedback for prioritizing negative feedback.
Input: input (customer feedback text)
Returns: sentiment, score, label
Use case: Call detectCustomerSentiment tool when analyzing customer reviews, complaints, suggestions."

Model (model)

Select AI model for sentiment analysis.

Field Properties:

Required field
Supports mainstream LLM providers
Model selection affects analysis accuracy

Recommended Models:

yaml

GPT-3.5-turbo: Cost-effective, suitable for most scenarios
GPT-4: Higher accuracy, complex sentiment recognition
Claude: Good at nuanced sentiment analysis

Input Text (input)

Text content to analyze sentiment. AI will extract and fill this field from conversation.

Field Properties:

Required field
Supports expressions
Supports multi-line text
AI automatically extracts text from conversation

Configuration Examples:

javascript

// 1. AI extracts from conversation
input: ""  // AI will automatically fill with user message

// 2. Get from context
input: $('Chat Trigger').message

// 3. Combine multiple information sources
input: `User feedback: ${$('Chat Trigger').message}
Historical reviews: ${$('Previous Reviews').averageRating}`

Neutral Sentiment Lower Bound (neutralLowerBound)

Lower bound score for neutral sentiment.

Field Properties:

Number type
Range: -1 to 1
Default: -0.2

Description: Scores within [neutralLowerBound, neutralUpperBound] are classified as neutral sentiment.

Neutral Sentiment Upper Bound (neutralUpperBound)

Upper bound score for neutral sentiment.

Field Properties:

Number type
Range: -1 to 1
Default: 0.2

Description:

Score > neutralUpperBound: Positive sentiment
Score < neutralLowerBound: Negative sentiment
Score between bounds: Neutral sentiment

Knowledge Base (knowledgeBaseId)

Optional, used to provide context reference for sentiment analysis.

Field Properties:

Optional field
Used for domain-specific sentiment analysis

Allow Improvise (allowImprovise)

Whether to allow AI to output custom sentiment labels beyond predefined sentiments.

Default: false

Custom Labels (customLabels)

Provide custom display labels for fields.

Field Properties:

Optional field
Key-value pair format

Advanced Settings (Settings Panel)

Always Output (alwaysOutput)

Whether to output empty item even on analysis failure.

Default: false

Execute Once (executeOnce)

Whether to execute only once using first input item.

Default: false

Retry on Fail (retryOnFail)

Whether to automatically retry on analysis failure.

Default: false

Max Tries (maxTries)

Maximum retries after failure.

Default: 3

Wait Between Tries (waitBetweenTries)

Wait time between retries (milliseconds).

Default: 1000 (1 second)

Node Description (nodeDescription)

Add custom description for the node.

yaml

nodeDescription: "Analyze sentiment tendency of customer feedback"

Output Data

Returns sentiment classification and score.

Output Structure:

javascript

{
  sentiment: "positive",  // Sentiment classification: positive/negative/neutral
  score: 0.85,           // Sentiment intensity score: -1 (extremely negative) to 1 (extremely positive)
  label: "Positive"      // Sentiment label
}

Access Output:

javascript

// Get sentiment classification
$('Sentiment Analysis Tool').sentiment

// Get sentiment intensity
$('Sentiment Analysis Tool').score

// Get sentiment label
$('Sentiment Analysis Tool').label

// Conditional judgment
$('Sentiment Analysis Tool').sentiment === "positive"

Workflow Examples

Example 1: Intelligent Customer Service Sentiment Recognition

Chat Trigger
  → AI Agent Node
    System Prompt: "You are an intelligent customer service assistant that needs to identify user emotions and provide corresponding service."
    
    Tools: [Sentiment Analysis Tool]
      Tool Name: "analyzeSentiment"
      Tool Description: "Analyze sentiment tendency of user messages. Input: input (user message). Returns: sentiment (sentiment classification), score (sentiment intensity), label (sentiment label). Call this tool when need to understand user emotions."
      Model: "GPT-3.5-turbo"
      Input: ""  // AI extracts from conversation
  → Answer Node

Conversation example:
User: "This product is terrible, I want a refund!"
AI: (Calls analyzeSentiment tool)
Analysis result: {sentiment: "negative", score: -0.8, label: "Negative"}
AI: "I'm very sorry for the inconvenience. I understand your dissatisfaction, let me handle the refund for you immediately..."

User: "Amazing, I'm very satisfied with this feature!"
AI: (Calls analyzeSentiment tool)
Analysis result: {sentiment: "positive", score: 0.9, label: "Positive"}
AI: "Glad to hear you're satisfied! Thank you for your feedback, we'll continue to work hard..."

Example 2: Emotional Conversation Routing

Chat Trigger
  → AI Agent Node
    System Prompt: "You are a customer service assistant providing different services based on user sentiment."
    
    Tools: [Sentiment Analysis Tool]
      Tool Name: "detectEmotion"
      Tool Description: "Detect user emotional state. Input: input. Returns: sentiment, score."
      Model: "GPT-4"
  → Conditional Branch
    Condition: $('Sentiment Analysis Tool').sentiment === "negative" && $('Sentiment Analysis Tool').score < -0.7
    → [True] → AI Agent (Urgent handling flow)
      System Prompt: "User emotion is very negative, need immediate comfort and problem solving"
    → [False] → Answer Node (Normal flow)

Example 3: Satisfaction Assessment

Chat Trigger
  → AI Agent Node
    System Prompt: "You are a customer satisfaction survey assistant."
    
    Tools: [Sentiment Analysis Tool]
      Tool Name: "evaluateSatisfaction"
      Tool Description: "Assess customer satisfaction. Input: input (customer feedback). Returns: sentiment, score."
      Model: "GPT-3.5-turbo"
  → Code Node
    Code: |
      const sentiment = $('Sentiment Analysis Tool');
      const satisfaction = sentiment.sentiment === "positive" && sentiment.score > 0.6
        ? "high"
        : sentiment.sentiment === "negative" && sentiment.score < -0.6
        ? "low"
        : "medium";
      
      return { satisfaction, ...sentiment };
  → Answer Node

Best Practices

1. Write Clear Tool Descriptions

Good tool description:

"Analyze sentiment tendency and intensity of user messages.
Input parameters:
- input: User message text
Returns:
- sentiment: Sentiment classification ("positive" | "negative" | "neutral")
- score: Sentiment intensity score (-1 to 1)
- label: Sentiment label

Use cases:
- Identify user emotional states
- Determine if urgent handling is needed
- Adjust reply strategy and tone

Example: Call analyzeSentiment tool when user expresses strong dissatisfaction or satisfaction."

2. Reasonably Set Neutral Sentiment Range

javascript

// 1. Default settings (relatively loose)
neutralLowerBound: -0.2
neutralUpperBound: 0.2

// 2. Strict settings (more biased toward clear sentiment)
neutralLowerBound: -0.1
neutralUpperBound: 0.1

// 3. Loose settings (more neutral judgments)
neutralLowerBound: -0.3
neutralUpperBound: 0.3

3. Guide AI Usage in System Prompt

javascript

System Prompt: `After calling sentiment analysis tool:
1. If sentiment === "negative" && score < -0.7:
   - Express understanding and apology
   - Provide solutions
   - Ask if further help is needed

2. If sentiment === "positive" && score > 0.7:
   - Express gratitude and happiness
   - Encourage continued use
   - Can recommend related products

3. If sentiment === "neutral":
   - Maintain professional and friendly tone
   - Answer questions normally`

4. Combine with Other Tools

javascript

Tools: [
  Tool 1 - Sentiment Analysis Tool
    Tool Name: "analyzeSentiment"
    Tool Description: "Analyze user sentiment"
  
  Tool 2 - HTTP Request Tool
    Tool Name: "escalateToHuman"
    Tool Description: "Escalate to human customer service"
  
  Tool 3 - Code Tool
    Tool Name: "recordFeedback"
    Tool Description: "Record user feedback"
]

// AI will decide whether to escalate to human or record feedback based on sentiment analysis results

FAQ

Q1: How does AI know when to call sentiment analysis tool?

A: AI decides based on:

Tool description: Whether it mentions need to identify user emotions
Conversation context: Whether user messages express strong emotions
Task requirements: Whether need to adjust strategy based on sentiment

Optimization advice:

Clearly state use cases in tool description
Provide call examples
Explain sentiment types being analyzed

Q2: How to handle mixed sentiment?

A: Sentiment analysis tool returns overall sentiment tendency:

javascript

// If user says "Product quality is good, but customer service attitude is poor"
// Tool will analyze overall sentiment, may return:
{
  sentiment: "negative",  // Overall biased toward negative
  score: -0.3,           // Medium negative intensity
  label: "Negative"
}

Q3: How to improve sentiment analysis accuracy?

1. Choose appropriate model

javascript

// Use stronger model for complex sentiment analysis
model: "GPT-4"  // Instead of GPT-3.5

2. Use knowledge base to provide context

javascript

knowledgeBaseId: "kb_customer_feedback"  // Domain-specific sentiment analysis

3. Adjust neutral sentiment range

javascript

// Adjust based on business requirements
neutralLowerBound: -0.1  // Stricter neutral judgment
neutralUpperBound: 0.1

Q4: Can sentiment analysis tool be called multiple times?

A: Yes! AI can call multiple times in same conversation:

User: "This product doesn't work well"
AI: (1st call) → Identifies negative sentiment
AI: "Sorry, can you describe the problem in detail?"

User: "Much better now, thanks!"
AI: (2nd call) → Identifies positive sentiment
AI: "Glad the problem is solved!"

Q5: How to take action based on sentiment analysis results?

Option 1: Guide in System Prompt

javascript

System Prompt: `Based on sentiment analysis results:
- Negative and strong (score < -0.7): Immediately provide solutions, escalate to human if necessary
- Positive and strong (score > 0.7): Express gratitude, can recommend related products
- Neutral: Handle normally`

Option 2: Combine with conditional branch

AI Agent (Calls sentiment analysis tool)
  → Conditional Branch
    Condition: sentiment === "negative" && score < -0.7
    → [True] → Escalate to human customer service
    → [False] → Continue conversation

Next Steps

AI Agent Node - Learn how to use AI Agents
Sentiment Analysis Action Node - Learn about Action version
Entity Recognition Tool Node - Extract information from conversation

HTTP Request Tool Node - Combine with API calls
Expression Syntax - Learn how to use expressions in configuration

Chat Trigger

Sentiment Analysis Tool Node ​

Use Cases ​

Typical Applications ​

Tool vs Action Differences ​

Node Configuration ​

Basic Settings (Parameters Panel) ​

Tool Name (toolName) ​

Tool Description (toolDescription) ​

Model (model) ​

Input Text (input) ​

Neutral Sentiment Lower Bound (neutralLowerBound) ​

Neutral Sentiment Upper Bound (neutralUpperBound) ​

Knowledge Base (knowledgeBaseId) ​

Allow Improvise (allowImprovise) ​

Custom Labels (customLabels) ​

Advanced Settings (Settings Panel) ​

Always Output (alwaysOutput) ​

Execute Once (executeOnce) ​

Retry on Fail (retryOnFail) ​

Max Tries (maxTries) ​

Wait Between Tries (waitBetweenTries) ​

Node Description (nodeDescription) ​

Output Data ​

Workflow Examples ​

Example 1: Intelligent Customer Service Sentiment Recognition ​

Example 2: Emotional Conversation Routing ​

Example 3: Satisfaction Assessment ​

Best Practices ​

1. Write Clear Tool Descriptions ​

2. Reasonably Set Neutral Sentiment Range ​

3. Guide AI Usage in System Prompt ​

4. Combine with Other Tools ​

FAQ ​

Q1: How does AI know when to call sentiment analysis tool? ​

Q2: How to handle mixed sentiment? ​

Q3: How to improve sentiment analysis accuracy? ​

Q4: Can sentiment analysis tool be called multiple times? ​

Q5: How to take action based on sentiment analysis results? ​

Next Steps ​

Related Resources ​

Sentiment Analysis Tool Node

Use Cases

Typical Applications

Tool vs Action Differences

Node Configuration

Basic Settings (Parameters Panel)

Tool Name (toolName)

Tool Description (toolDescription)

Model (model)

Input Text (input)

Neutral Sentiment Lower Bound (neutralLowerBound)

Neutral Sentiment Upper Bound (neutralUpperBound)

Knowledge Base (knowledgeBaseId)

Allow Improvise (allowImprovise)

Custom Labels (customLabels)

Advanced Settings (Settings Panel)

Always Output (alwaysOutput)

Execute Once (executeOnce)

Retry on Fail (retryOnFail)

Max Tries (maxTries)

Wait Between Tries (waitBetweenTries)

Node Description (nodeDescription)

Output Data

Workflow Examples

Example 1: Intelligent Customer Service Sentiment Recognition

Example 2: Emotional Conversation Routing

Example 3: Satisfaction Assessment

Best Practices

1. Write Clear Tool Descriptions

2. Reasonably Set Neutral Sentiment Range

3. Guide AI Usage in System Prompt

4. Combine with Other Tools

FAQ

Q1: How does AI know when to call sentiment analysis tool?

Q2: How to handle mixed sentiment?

Q3: How to improve sentiment analysis accuracy?

Q4: Can sentiment analysis tool be called multiple times?

Q5: How to take action based on sentiment analysis results?

Next Steps

Related Resources