Customer comment sentiment analysis
Automatic sentiment analysis of customer comments with theme extraction, trends, and product recommendations.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
J'ai [NOMBRE_COMMENTAIRES] commentaires clients à analyser pour [PRODUIT/SERVICE] provenant de [SOURCE] (ex : Trustpilot, App Store, enquête, support chat, réseaux sociaux). Les commentaires sont en [LANGUE] et couvrent la période [PERIODE].
Objectif de l'analyse : [OBJECTIF] (ex : identifier les points de douleur, préparer la feuille de route produit)
Réalise une analyse de sentiment complète :
- Classifie chaque commentaire : positif / négatif / neutre avec score de confiance
- Extrait les entités et thèmes clés mentionnés (features, aspects du service, équipe)
- Construit une matrice aspect-sentiment : quels aspects génèrent satisfaction vs frustration
- Identifie les tendances temporelles : le sentiment s'améliore-t-il ou se dégrade-t-il
- Détecte les émotions au-delà du positif/négatif (joie, colère, surprise, déception)
- Identifie les commentaires les plus représentatifs de chaque catégorie
- Propose [N] améliorations produit/service basées sur les insights extraits
- Génère un rapport de synthèse exécutif en une page
Outil préféré : [OUTIL] (Python NLTK/transformers, API Claude/OpenAI, Google NLP)
Format des données en entrée : [FORMAT] (CSV, JSON, texte brut)
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Why this prompt works
<p>This prompt is effective because it goes beyond simple positive/negative classification to include aspect-level analysis and temporal trends, providing actionable insights for product and customer service.</p>
Use Cases
Expected Output
Sentiment classification, aspect-sentiment matrix, temporal trends, product recommendations, and executive report.
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Run this prompt through the Optimizer to strengthen its context, constraints and expected format.
Improve this prompt with the OptimizerComments
- LéaAI
Pour affiner la détection émotionnelle, ajoutez un glossaire de termes métier propres à votre secteur (ex: 'bug' → colère, 'interface intuitive' → joie). Cela évite les biais des modèles génériques.
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