Sentiment Analyzer

Sentiment Analyzer

Classifies the emotional tone and opinion expressed in text at multiple granularities — document-level, sentence-level, and aspect-level — with confidence scores and supporting evidence. Handles reviews, support tickets, social media posts, survey responses, and any opinion-bearing text.

When to Use

• User provides customer reviews, survey responses, or feedback and wants sentiment scores
• Support ticket queue needs to be triaged by urgency or customer frustration level
• Social media posts or brand mentions need to be monitored for sentiment trends
• User asks to "analyze tone", "detect emotion", or "measure satisfaction" in text
• A/B test copy needs to be evaluated for emotional impact
• Product or service feedback needs to be categorized for product teams
• NPS comments or CSAT responses need structured analysis

Process

1. Determine the analysis scope:

   • Document-level: single overall sentiment for the entire text
   • Sentence-level: per-sentence sentiment breakdown
   • Aspect-level: sentiment for specific entities or attributes mentioned (e.g., "price: negative", "delivery: positive")
   • Emotion detection: beyond polarity — joy, anger, fear, surprise, sadness, disgust
   • Default to document + aspect level for product reviews; document level for short texts

2. Preprocess the text:

   • Detect and handle: sarcasm signals (detect but flag with low confidence), negation ("not great" → negative), intensifiers ("absolutely terrible" → strongly negative), and qualified statements ("usually good, but this time...").
   • Identify the language and adjust analysis accordingly
   • Note: emojis, slang, and abbreviations are valid sentiment signals — interpret them contextually

3. Classify overall sentiment:

   • Return one of: Positive, Negative, Neutral, Mixed
   • Assign a confidence score (0–1) and a normalized sentiment score (-1.0 to +1.0)
   • Mixed = significant positive AND negative sentiment present in the same text

4. Perform aspect-level analysis (when applicable):

   • Identify mentioned aspects/entities: price, quality, service, delivery, usability, staff, features, etc.
   • For each aspect: extract the relevant snippet, classify sentiment, and assign a score
   • Map to a structured output so results can be aggregated across many texts

5. Extract key phrases:

   • Quote the specific phrases that most strongly drove the sentiment classification
   • Distinguish positive driver phrases from negative driver phrases

6. Handle edge cases:

   • Sarcasm: flag as potentially sarcastic if linguistic signals are present; lower confidence
   • Neutral text (factual statements, questions): classify as Neutral with explanation
   • Multilingual text: detect and process each language segment separately
   • Ambiguous text: present both possible interpretations with probability estimates

7. Batch processing (for multiple inputs):

   • Return an array of results, one per input
   • Include aggregate statistics: sentiment distribution, average score, most common aspects mentioned

Output Format

Single Document Analysis

{
  "text": "The product quality is outstanding, but shipping took two weeks longer than promised and customer service was unhelpful.",
  "overall_sentiment": "Mixed",
  "sentiment_score": -0.12,
  "confidence": 0.88,
  "aspects": [
    { "aspect": "product quality", "sentiment": "Positive", "score": 0.92, "snippet": "product quality is outstanding" },
    { "aspect": "shipping", "sentiment": "Negative", "score": -0.75, "snippet": "took two weeks longer than promised" },
    { "aspect": "customer service", "sentiment": "Negative", "score": -0.68, "snippet": "customer service was unhelpful" }
  ],
  "key_phrases": {
    "positive": ["outstanding"],
    "negative": ["two weeks longer than promised", "unhelpful"]
  }
}

Batch Summary (10+ documents)

Sentiment Distribution: 62% Positive · 28% Negative · 7% Mixed · 3% Neutral
Average Score: +0.41

Top Positive Aspects: product quality (87%), ease of use (74%), value for money (65%)
Top Negative Aspects: shipping speed (43%), return process (38%), customer support (31%)

Flagged for Review (frustrated customers):
- Review #42: Score -0.91 ("This is the worst experience I've ever had...")
- Review #87: Score -0.85 ("Complete waste of money. Never again.")

Examples

Example Input

Analyze the sentiment of these three customer reviews and tell me which aspects each mentions:

1. "Absolutely love the new interface — so clean and fast!"
2. "Decent product but overpriced. The support team was slow to respond."
3. "Works fine I guess. Nothing special."

Example Output

Review 1:
  Overall: Positive (score: +0.93)
  Aspects: interface → Positive ("clean and fast")

Review 2:
  Overall: Mixed (score: -0.22)
  Aspects: product quality → Neutral ("decent"), price → Negative ("overpriced"), customer support → Negative ("slow to respond")

Review 3:
  Overall: Neutral (score: +0.05)
  Aspects: none clearly identified
  Note: Understated tone — low expressiveness, not necessarily indifferent.

Boundaries

• Do NOT conflate factual statements with sentiment — "The battery lasts 4 hours" is Neutral unless paired with a judgment.
• Flag sarcasm explicitly and reduce confidence rather than silently inverting the apparent polarity.
• Do NOT apply sentiment models trained on English to other languages without language detection — always detect language first.
• For mental health or crisis-related text, flag urgency and suggest the user route to appropriate human support rather than reducing it to a sentiment score.
• Confidence scores are estimates, not ground truth — communicate this when presenting scores to stakeholders.
• Do NOT use sentiment analysis results to make high-stakes decisions (hiring, loan approvals) without human review.