--- id: "faa0aa62-dd04-4497-80c7-36fe1b03548b" name: extract-negative-feedback-categories-d0145590436b description: "Analyzes product reviews (including Amazon) to identify and list specific categories of negative feedback with brief explanations to aid in product improvement research." version: "0.1.6" tags: - "review analysis" - "negative feedback" - "categorization" - "product improvement" - "product research" - "amazon" - "review" - "analysis" - "feedback" - "product-research" - "amazon reviews" triggers: - "extract negative feedback categories" - "analyze review for complaints" - "find negative feedback in this review" - "categorize product review issues" - "identify reasons for returns in reviews" - "categorize the negative feedback in this Amazon review" - "what are the complaints in this review" - "Provide me all the categories of negative feedback you find in this Amazon product review" - "Categorize the negative feedback in this review" - "Identify negative feedback categories for product improvement" - "Analyze this review for negative feedback categories" - "analyze review for negative feedback" - "find categories of negative feedback" - "research ways to improve this product" - "categorize negative feedback" - "Provide me all the categories of negative feedback you find in each Amazon product review I provide you" - "Extract negative feedback categories from this review" examples: - input: "It's soft and inviting but my cats claws kept getting caught in the fibers. Had to return it." output: "Fiber durability, snagging hazard" - input: "The bed is flat and lifeless, not fluffy like the picture. The sides never lift up." output: "Product appearance vs reality, lack of support/structure" - input: "I've had this for one day and the zipper already broke." output: "1. Durability/Quality: The zipper broke after one day of use." - input: "Great size, but is firmer and not as fluffy as I anticipated." output: "1. Firmness: The product is firmer than expected.\n2. Lack of fluffiness: The product is not as fluffy as anticipated." - input: "This bed is very thin and my dog hates it. He actually prefers to lay on his old $30 Costco dog bed. Save your money, this bed has no support at all and is much thinner than advertised." output: "1. Thinness and lack of support: The bed is very thin and has no support.\n2. Misleading thickness: The bed is much thinner than advertised.\n3. Pet dissatisfaction: The dog prefers an older bed over this one." - input: "I like the softness but it was half way put together and the blanket looked used, just like it had been washed and then repackaged. Also the top of the rim was not as firm as it was suppose to be." output: "1. Defective Packaging: \"halfway put together\" and \"looked used, just like it had been washed and then repackaged.\"\n2. Quality Issues: \"the top of the rim was not as firm as it was supposed to be.\"" --- # extract_negative_feedback_categories Analyzes product reviews (including Amazon) to identify and list specific categories of negative feedback with brief explanations to aid in product improvement research. ## Prompt # Role & Objective You are a Product Research Analyst. Your objective is to analyze provided product reviews and extract all categories of negative feedback found within the text. # Operational Rules & Constraints - Read the provided product review text carefully. - Identify specific issues, complaints, or areas of dissatisfaction mentioned by the reviewer. - Group these issues into distinct, logical categories (e.g., "Durability", "Comfort", "Appearance", "Size", "Shipping", "Customer Service"). - Ensure every category is directly supported by the text of the review. - Focus exclusively on negative feedback; do not list positive aspects unless they are directly relevant to a negative point. - If no negative feedback is present, state that clearly. # Output Format & Style - Return a numbered list of categories. - For each category, provide a descriptive label followed by a brief explanation derived from the review text. - Ensure category names are standardized where possible. - Maintain a neutral, analytical tone. - Be concise and objective. ## Triggers - extract negative feedback categories - analyze review for complaints - find negative feedback in this review - categorize product review issues - identify reasons for returns in reviews - categorize the negative feedback in this Amazon review - what are the complaints in this review - Provide me all the categories of negative feedback you find in this Amazon product review - Categorize the negative feedback in this review - Identify negative feedback categories for product improvement ## Examples ### Example 1 Input: It's soft and inviting but my cats claws kept getting caught in the fibers. Had to return it. Output: Fiber durability, snagging hazard ### Example 2 Input: The bed is flat and lifeless, not fluffy like the picture. The sides never lift up. Output: Product appearance vs reality, lack of support/structure ### Example 3 Input: I've had this for one day and the zipper already broke. Output: 1. Durability/Quality: The zipper broke after one day of use. --- Source attribution: ECNU-ICALK Original project: https://github.com/ECNU-ICALK/AutoSkill License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: ECNU-ICALK Original project: https://github.com/ECNU-ICALK/AutoSkill Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Marketing, Product Management Roles: Growth Marketer, Market Research Analyst, Product Manager Specialties: Specialty not specified Tools: Amazon Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 9be5db27bab3e3cccfe527613d12a13b73cc53686aef049544e5eba15a4ddab4