💡 What We Are
People Have Spoken is an independent platform for product analysis. We analyze thousands of real user reviews to find out what people actually think.
🤔 Why We Exist
Finding trustworthy product information is difficult. It’s hard to sort through biased opinions, expert reviews, and confusing technical specs.
Our mission is to provide clear, data-driven analysis. We help you make better buying decisions by showing you what real owners truly think.
⚙️ How It Works
Our analysis is guided by real data, not anecdote. We follow a systematic process to turn thousands of raw user reviews into a clear, insightful, and trustworthy report.
1️⃣ Define the Playing Field We start by defining a product category (e.g., “Smartphones”). We then compile a comprehensive list of all relevant models in that market to ensure we have a complete picture.
2️⃣ Gather & Clean Reviews We collect thousands of verified user reviews for each product. Critically, we then “clean” this data by filtering out irrelevant feedback.
Example: We remove reviews that only complain about shipping, are too short to be useful, or are from secondhand users. This ensures we are only analyzing genuine opinions about the product itself.
3️⃣ Build the Analytical Framework We analyze a sample of the cleaned reviews to discover the specific Aspects (like “Camera”) and Factors (like “Low-light performance”) that users consistently mention. This becomes the official “schema” we use to measure every product in that category.
4️⃣ Analyze Every Opinion Our system reads every single review and tags each opinion according to our framework. We quantify the user sentiment for each factor, classifying it as Positive, Negative, or Neutral.
Example: A quote like “The battery lasts two full days!” is tagged as {Aspect: “Battery”, Factor: “Endurance”, Sentiment: “Positive”}.
5️⃣ Create Scores & Comparisons We aggregate thousands of these tags to generate a final positive sentiment score (%) for each aspect. We then compare these scores against direct competitors, predecessor models, and the category average to provide crucial context.
6️⃣ Deliver the Final Insight Finally, we write a detailed analysis explaining the story behind the data. We use the scores and direct user quotes to build a narrative that gives you a clear understanding of a product’s true strengths and weaknesses.