Sora Prompt for Analyzing Customer Reviews
Analyzing customer reviews is a strategic lever for any business looking to improve its products, services, and user experience. With Sora, you can transform hundreds or even thousands of customer feedbacks into actionable insights in minutes. Rather than manually reading each comment, Sora allows you to identify recurring trends, categorize sentiments, and prioritize improvement areas. Whether your reviews come from Google, Trustpilot, Amazon, or your internal surveys, a well-structured prompt helps extract a synthetic and actionable view. On this page, we offer a main optimized prompt along with variants adapted to your expertise level to fully leverage Sora's power in analyzing your customer reviews. The goal: turn raw, scattered data into a clear, prioritized qualitative dashboard directly usable by your product, marketing, and support teams.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
You are an expert analyst in customer experience and natural language processing. I will provide you with a set of customer reviews. For each batch of reviews, perform the following analysis:
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Sentiment Classification: Categorize each review as positive, neutral, or negative, with a confidence score (0-100%).
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Theme Extraction: Identify the 5 to 10 main themes addressed (e.g., product quality, customer service, delivery, value for money, user interface).
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Strengths/Weaknesses Matrix: Create a summary table of the most frequently mentioned strengths and weaknesses, ranked by frequency.
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Weak Signals: Identify rare but potentially critical mentions (bugs, security issues, emerging feature requests).
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Priority Recommendations: Propose 3 to 5 concrete actions ranked by estimated impact and ease of implementation.
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Executive Summary: Write a summary of no more than 150 words intended for a leadership team.
Output format: clear section structure with markdown tables where relevant.
Here are the reviews to analyze:
[PASTE_YOUR_REVIEWS_HERE]
Personalize this prompt with Léa
Answer 3 questions and Léa tailors the prompt to your situation.
Why this prompt works
This prompt works by assigning an expert role that sets the analytical tone, combined with a six-step sequential structure that forces thorough and methodical analysis. The explicit request for a structured output format (markdown tables, executive summary) guarantees directly usable results without reprocessing. Finally, the inclusion of weak signals pushes the model beyond superficial analysis to detect high-value insights.
Use Cases
Variants
Expected Output
You will receive a comprehensive analysis report including sentiment classification per review, a table of main themes with their frequency, a visual strengths/weaknesses matrix, and a prioritized list of recommendations. The 150-word executive summary allows you to quickly share key conclusions with your management or stakeholders without them having to read the full report.
Frequently Asked Questions
How many customer reviews can I analyze at once with Sora?
The limit depends on the model's context window. In practice, you can analyze between 50 and 200 reviews per request, depending on their length. For larger volumes, split your reviews into batches and request a consolidated summary at the end. A handy tip is to pre-format your reviews by numbering each entry to make referencing them in the analysis easier.
How should I prepare my customer reviews before submitting them to Sora?
For best results, structure your reviews with a clear separator between each entry (dashes, numbering, or blank lines). Remove duplicates and obvious spam reviews. If possible, include the date and source of each review to allow for temporal and comparative analysis. A simple copy-paste from a spreadsheet with one column per review works very well.
Are the sentiment analysis results reliable for reviews in French?
Sora handles French very well, including colloquial expressions, mild irony, and complex negative phrasing (e.g., 'pas mal' as a compliment). However, heavy sarcasm and highly specific slang can be misinterpreted. To improve reliability, you can specify the cultural context and customer type in your prompt. Across large volumes, the sentiment classification accuracy rate typically exceeds 85%.
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