Feedback & Review Analyzer

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Powered by AI / LLM Insights You Can Trust

Reading 1,000 customer reviews manually takes 8 hours. Hiring an analyst costs $500+. This Actor does the same job in minutes — at a fraction of the cost — and gives you back data that's ready to drop into a dashboard, report, or model.

How it works

You give it raw reviews from any source — Amazon, App Store, Trustpilot, Shopify, support tickets, surveys. It returns one enriched row per review with:

• Sentiment — Positive / Negative / Mixed with confidence score
• What customers loved — specific aspects, not vague themes
• What customers complained about — concrete issues
• Key topics they actually mention
• Fake review flags with transparent reasoning
• English translation from 100+ languages
• Data quality score per record

What you get per entry

{
"review_id":1,
"original_text":"Excellent product, the battery lasts 12 hours and the sound quality is crisp. Worth every penny.",
"translated_text":"Excellent product, the battery lasts 12 hours and the sound quality is crisp. Worth every penny.",
"original_language":"en",
"sentiment":"Positive",
"confidence":85,
"positive_aspects":["battery life","sound quality"],
"negative_aspects":[],
"key_topics":["product quality"],
"fake_review_flag":false,
"fake_signals":[],
"fake_detection_method":"Text-based signals only. Cannot detect coordinated campaigns or burst patterns.",
"explanation":"The review highlights specific positive features of the product, indicating satisfaction.",
"quality_score":100
}

Input format

{
"records":[
{"text":"Excellent product, the battery lasts 12 hours and the sound quality is crisp. Worth every penny."},
{"text":"Total waste of money. Stopped working after 2 weeks and customer support was unresponsive."},
{"text":"Great features but the build quality feels cheap. Mixed feelings overall."}
]
}

Built for serious work

• Multi-language: 100+ languages auto-translated before analysis
• PII removal: Emails, phone numbers, URLs stripped automatically
• Production-grade security: Multi-layer prompt injection defense — adversarial inputs flagged, not processed
• Quality scoring: Every record gets a 0-100 completeness score

Limits

• Max 1,000 entries per run
• Max 3,000 characters per entry
• Suspicious or manipulative input is flagged and skipped

Best for

• E-commerce sellers analyzing product reviews
• Product managers tracking feature feedback
• Market researchers comparing competitor reviews
• App developers monitoring App Store / Play Store reviews
• AI teams preparing clean training data

Powered by

• Llama 3.3 70B via Groq for fast accurate analysis
• Pandas pipelines for cleaning and validation
• Google Translate for multi-language support
• Custom injection defense layer built on 30+ pattern matchers