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Sora Prompt to Analyze Customer Reviews

Analyzing customer reviews is a strategic lever for any company looking to improve its products, services, and user experience. With Sora, you can transform hundreds or even thousands of customer feedback into actionable insights in minutes. Rather than manually reading each comment, Sora allows you to identify recurring trends, categorize sentiments, and highlight priority areas for improvement. Whether your reviews come from Google, Trustpilot, Amazon, or your internal surveys, a well-structured prompt helps extract a synthetic and actionable view. In this page, we offer an optimized main prompt as well as variants tailored 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.

The prompt

Sora

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:

  1. Sentiment Classification: Categorize each review as positive, neutral, or negative, with a confidence score (0-100%).

  2. Theme Extraction: Identify the 5 to 10 main themes discussed (e.g., product quality, customer service, delivery, value for money, user interface).

  3. Strengths/Weaknesses Matrix: Create a summary table of the most mentioned strengths and weaknesses, sorted by frequency.

  4. Weak Signals: Spot rare but potentially critical mentions (bugs, security issues, emerging feature requests).

  5. Priority Recommendations: Propose 3 to 5 concrete actions ranked by estimated impact and ease of implementation.

  6. Executive Summary: Write a summary of up to 150 words intended for a leadership team.

Output format: clear sections with markdown tables where relevant.

Here are the reviews to analyze:
[PASTE_YOUR_REVIEWS_HERE]

Personalize this prompt with Léa

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Why it works

This prompt works by assigning an expert role that sets the analytical tone, combined with a six-step sequential structure that forces exhaustive 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.

Expected result

You will get a comprehensive analysis report including sentiment classification per review, a table of main themes with their frequency, a visual strengths/weaknesses matrix, and a list of prioritized recommendations. The 150-word executive summary allows you to quickly share key findings with your management or stakeholders without them having to read the full report.

Variants by level

FAQ

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 ask for a consolidated summary at the end. A tip: pre-format your reviews by numbering each entry to facilitate referencing in the analysis.
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 temporal and comparative analysis. A simple copy-paste from a spreadsheet with one column per review works well.
Are the sentiment analysis results reliable for French reviews?
Sora handles French very well, including colloquial expressions, light irony, and complex negative constructions (e.g., 'not bad' as a compliment). However, heavy sarcasm and very specific slang may be misinterpreted. To improve reliability, you can specify the cultural context and customer type in your prompt. On large volumes, sentiment classification accuracy typically exceeds 85%.

How to use this prompt

  1. Copy the prompt with the button above.
  2. Paste it into ChatGPT, Claude or your favorite AI assistant.
  3. Replace the bracketed variables with your details, then refine the result.

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