Sentiment Analysis

Determine the sentiment (positive, negative, neutral) of text using an LLM.

Written By pvdyck

Last updated About 3 hours ago

Sentiment Analysis

The Sentiment Analysis node uses an LLM to classify the emotional tone of input text into sentiment categories.

How It Works

The node sends text to a connected language model with built-in prompting that asks the model to determine sentiment. The output is a structured classification you can use for routing or reporting.

Parameters

ParameterDescription
TextThe input text to analyze. Supports expressions like {{ $json.message }}.
CategoriesSentiment categories to classify into. Default: Positive, Negative, Neutral. Customizable -- you can add fine-grained categories like Excited, Happy, Neutral, Disappointed, Angry.
Include Detailed ResultsAdds sentiment strength and confidence scores to the output. Note: these are model-generated estimates, not precise measurements.
System Prompt TemplateCustomize the analysis prompt. Use the {categories} placeholder to insert your category list into the prompt.
Enable Auto-FixingAutomatically re-prompts the model if the output doesn't match expected categories.

Output

The node returns:

  • Sentiment — The classified category (e.g., "Positive", "Negative", "Neutral").
  • Confidence — How confident the model is in its classification (when supported by the model).

Sub-Node Connections

InputRequiredDescription
AI Language ModelYesThe LLM powering the analysis (e.g., OpenRouter, OpenAI).
Output ParserNoAdditional output formatting.

Example Use Cases

  • Classify customer feedback before routing to support teams
  • Monitor social media mentions for brand sentiment
  • Triage support tickets by urgency/emotion
  • Score NPS survey responses automatically

Tips

  • Set the language model temperature to 0 (or near-zero) for consistent, reproducible sentiment classification.
  • Customize categories for your domain (e.g., "Urgent", "Frustrated", "Satisfied" for support tickets).
  • Use the System Prompt Template to handle edge cases like sarcasm or mixed sentiment.
  • Split large text volumes into smaller chunks for more efficient processing.
  • Chain with a Switch node to route messages based on sentiment result.
  • Performance varies by input language -- verify the connected model supports your target language.

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