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.
Usage
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:
Benchmarks
Environment:
- Platform: Amazon Linux 2
- Python Version: 3.11.6
Run the following script:
python benchmarks/run.py input Sentiment
Results:
| 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 |