Sentiment Analysis

Description

Analyze a given text to determine its sentiment, providing a detailed breakdown of positive, negative, and neutral elements, along with an overall sentiment score and analysis of undertones.

Project compatibility

Windows | Cross-platform

Configuration

• Connection ID - The connection established in Integration Service. Access the dropdown menu to choose, add, or manage connections.

• Text - The text to be analyzed for sentiment. This field supports String type input.

Manage Properties

Use the Manage Properties wizard to configure or use any of the object's standard or custom fields. You can select fields to add them to the activity canvas. The added standard or custom fields are available in the Properties panel (in Studio Desktop) or under Show additional properties (in Studio Web).

Additional properties

Output

• Overall Sentiment - Contains the sentiment score and label:
  • Very Negative: -99 to -67
  • Negative: -66 to -34
  • Slightly Negative: -33 to -1
  • Neutral: 0
  • Slightly Positive: 1 to 33
  • Positive: 34 to 66
  • Very Positive: 67 to 99
• Sentiment Breakdown - Counts of positive, negative, neutral, and total statements.
• Confidence Level - The overall confidence level of the analysis.
• Key Phrases - A string of key phrases identified in the sentiment analysis.
• Analysis - Detailed explanation of the sentiment analysis.
• Undertones - Subtle undertones detected in the text with their impact.
• Sentiment Analysis - Automatically generated output variable.

Output JSON format:

{
  "overallSentiment": {
    "score": 0,
    "label": ""
  },
  "sentimentBreakdown": {
    "positiveStatements": 0,
    "negativeStatements": 0,
    "neutralStatements": 0,
    "totalStatements": 0
  },
  "confidenceLevel": 0,
  "keyPhrases": [
    {
      "phrase": "",
      "sentiment": "",
      "confidence": 0
    }
  ],
  "analysis": "",
  "undertones": [
    {
      "description": "",
      "impact": ""
    }
  ]
}

Limitations and other considerations

• The complexity and length of the input text may affect the accuracy of the analysis.

• The system may have difficulties with highly context-dependent or culturally specific expressions.

• Sarcasm, irony, and other forms of figurative language may pose challenges for accurate sentiment detection.

• The system's performance may vary across different languages or dialects.