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Sentiment Scanner

It scans and evaluates the overall sentiment of prompts using the SentimentIntensityAnalyzer from the NLTK (Natural Language Toolkit) library.

Attack scenario

The primary objective of the scanner is to gauge the sentiment of a given prompt. Prompts with sentiment scores below a specified threshold are identified as having a negative sentiment. This can be especially useful in platforms where monitoring and moderating user sentiment is crucial.

How it works

The sentiment score is calculated using nltk's Vader sentiment analyzer. The SentimentIntensityAnalyzer produces a sentiment score ranging from -1 to 1:

  • -1 represents a completely negative sentiment.
  • 0 represents a neutral sentiment.
  • 1 represents a completely positive sentiment.

By setting a predefined threshold, the scanner can be calibrated to flag any prompts falling below that threshold, indicating a potentially negative sentiment.


from llm_guard.input_scanners import Sentiment

scanner = Sentiment(threshold=0)
sanitized_prompt, is_valid, risk_score = scanner.scan(prompt)

For a deeper understanding of the sentiment analysis process and its underlying methods, consult:



  • Platform: Amazon Linux 2
  • Python Version: 3.11.6

Run the following script:

python benchmarks/ input Sentiment


Instance Input Length Test Times Latency Variance Latency 90 Percentile Latency 95 Percentile Latency 99 Percentile Average Latency (ms) QPS
AWS m5.xlarge 225 5 0.00 0.55 0.58 0.60 0.49 456765.43
AWS g5.xlarge GPU 225 5 0.00 0.51 0.53 0.55 0.45 497964.10
Azure Standard_D4as_v4 225 5 0.0 0.67 0.70 0.72 0.59 380511.97