Skip to main content

Documentation Index

Fetch the complete documentation index at: https://handbook.fiddler.ai/llms.txt

Use this file to discover all available pages before exploring further.

Evaluator to assess text sentiment using Fiddler’s sentiment analysis model. The Sentiment evaluator uses Fiddler’s implementation of the cardiffnlp/twitter-roberta-base-sentiment-latest model to evaluate the sentiment polarity of text content. This evaluator helps identify the emotional tone and attitude expressed in text, providing both sentiment labels and confidence scores for sentiment classification. Key Features:
  • Sentiment Classification: Evaluates text for positive, negative, or neutral sentiment
  • Dual Score Output: Returns both sentiment label and probability confidence
  • Fiddler Integration: Leverages Fiddler’s optimized sentiment evaluation model
  • Multi-Score Output: Returns both sentiment label and probability scores
Sentiment Categories Evaluated:
  • sentiment: The predicted sentiment label (positive, negative, neutral)
  • sentiment_prob: Probability score (0.0-1.0) for the predicted sentiment
Use Cases:
  • Social Media Monitoring: Analyzing sentiment in tweets, posts, and comments
  • Customer Feedback Analysis: Understanding customer satisfaction and opinions
  • Brand Monitoring: Tracking public sentiment about products or services
  • Content Moderation: Identifying emotionally charged or problematic content
  • Market Research: Analyzing public opinion and sentiment trends
Scoring Logic: : The sentiment evaluation provides two complementary scores:
  • sentiment: The predicted sentiment label<br>
    • “positive”: Text expresses positive emotions or opinions
    • “negative”: Text expresses negative emotions or opinions
    • “neutral”: Text expresses neutral or balanced sentiment
  • sentiment_prob: Confidence score (0.0-1.0) for the prediction : - 0.0-0.3: Low confidence in sentiment prediction
  • 0.3-0.7: Medium confidence in sentiment prediction
  • 0.7-1.0: High confidence in sentiment prediction

​
Parameters

ParameterTypeRequiredDefaultDescription
textstr✗NoneThe text content to evaluate for sentiment.

​
Returns

A list of Score objects containing: : - sentiment: Score object with sentiment label (positive/negative/neutral)
  • sentiment_prob: Score object with probability score (0.0-1.0)
Return type: list[Score]

​
Raises

ValueError — If the text is empty or None, or if no scores are returned from the API.

​
Example

from fiddler_evals.evaluators import Sentiment
evaluator = Sentiment()

# Positive sentiment
scores = evaluator.score("I love this product! It's amazing!")
print(f"Sentiment: {scores[0].label}")
print(f"Confidence: {scores[1].value}")
# Sentiment: positive
# Confidence: 0.95

# Negative sentiment
negative_scores = evaluator.score("This is terrible and disappointing!")
print(f"Sentiment: {negative_scores[0].label}")
print(f"Confidence: {negative_scores[1].value}")
# Sentiment: negative
# Confidence: 0.88

# Neutral sentiment
neutral_scores = evaluator.score("The weather is okay today.")
print(f"Sentiment: {neutral_scores[0].label}")
print(f"Confidence: {neutral_scores[1].value}")
# Sentiment: neutral
# Confidence: 0.72

# Filter based on sentiment and confidence
if scores[0].label == "positive" and scores[1].value > 0.8:

    print("High confidence positive sentiment detected")
This evaluator is optimized for social media and informal text analysis using the cardiffnlp/twitter-roberta-base-sentiment-latest model. It performs best on short, conversational text similar to Twitter posts. For formal or academic text, consider using specialized sentiment analysis models. The dual-score output provides both categorical classification and confidence assessment for robust sentiment analysis workflows.

​
name = ‘sentiment_analysis’

​
score()

Score the sentiment of text content.

​
Parameters

ParameterTypeRequiredDefaultDescription
textstr✗NoneThe text content to evaluate for sentiment.

​
Returns

A list of Score objects for sentiment label and probability. Return type: list[Score]