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Слепая оценка нового чая стартапа
Обзор/Оценка
30
30
Обработать 100 офлайн слепых оценок потребителей для 3 новых сортов чая стартапа, выделить плюсы, минусы и рекомендации по улучшению продукта.
Содержание промпта
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Sort 100 anonymous offline consumer blind reviews for three new sugar-free oolong tea drinks ready to launch from a startup tea brand, categorize positive and negative comments about taste, aroma, after-sweetness and aftertaste, summarize the most prominent advantages and issues to adjust for each product, and provide clear optimization directions for the brand.
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Обработайте 100 анонимных офлайн слепых оценок потребителей для трех новых сортов нефильтрованного улуна, которые готовит к запуску стартап чайного бренда, распределите по категориям положительные и отрицательные отзывы о вкусе, аромате, послевкусии и остаточной сладости, суммируйте самые заметные преимущества и проблемы для корректировки каждого продукта и предоставьте бренду четкие направления оптимизации.
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Рекомендация
Анализ тональности отзывов
Please analyze the sentiment polarity of the provided e-commerce user review texts, identifying whether each comment is positive, negative, or neutral. Extract specific product features or issues mentioned, such as quality, logistics, or service, and generate a brief report including sentiment distribution statistics, key pain point summaries, and improvement suggestions to help merchants understand user feedback and optimize their products and services.
Анализ тональности отзывов
Act as a senior e-commerce data analyst to perform deep sentiment polarity determination and key element extraction on the provided user review text. First, identify the emotional polarity (positive, negative, or neutral) expressed in the review and quantify its intensity. Second, accurately extract specific viewpoints involving product functions, logistics experience, and after-sales service dimensions from the text. Finally, combine the context to determine if there is sarcasm or implicit dissatisfaction, and output a structured sentiment analysis report to help merchants quickly locate service pain points and product advantages, without using any structured templates or stacked imperative verbs, directly narrating the analysis process and conclusions in natural language.
Восстановление отзывов
Act as a senior e-commerce customer service expert to draft a response for a specific negative user review. This review typically expresses dissatisfaction with product quality, shipping speed, or after-sales attitude. Your task is to write a reply based on the user's specific complaint that demonstrates sincere apology while offering a tangible solution. The response should avoid robotic apologies; instead, it must specifically address the pain points mentioned (such as damage, delay, or functional defects) and provide clear remedial actions (such as refunds, replacements, or coupons). The tone should be professional, gentle, and firm, aiming to convert negative emotions into positive experiences while protecting the brand reputation, ensuring the reply complies with platform regulations and carries no legal risks.
Обработчик отзывов для электронной коммерции
Act as a senior e-commerce customer service manager to analyze and draft a response for the following specific negative user review. The review involves issues with product quality and logistics speed, with an agitated tone. You need to first empathize with the user's disappointment, objectively acknowledge the service shortcomings, then provide a specific compensation or solution plan, and finally invite the user to experience the brand again with sincerity. Ensure the response maintains a professional brand image while effectively calming the user, using natural language that conforms to business communication norms in the Chinese context.
Ответ на отзыв в электронной коммерции
Act as a senior e-commerce customer service expert. Draft a response to the following negative product review. The reply must first sincerely apologize and acknowledge the customer's specific dissatisfaction without shifting blame. Second, briefly explain the corrective actions taken or the proposed solution to demonstrate seriousness about the issue. Finally, offer specific compensation or follow-up service options. Maintain a gentle, professional, and empathetic tone to restore trust and showcase brand responsibility, strictly avoiding robotic templates or confrontational language.
Анализ тональности отзывов
Act as a senior e-commerce data analyst to perform deep sentiment assessment and key element extraction on provided user review texts. First, identify the core sentiment polarity (positive, negative, or neutral) and quantify its intensity. Second, accurately extract specific dimensional evaluations regarding product quality, logistics speed, customer service attitude, and cost-performance from the text. Finally, based on the contextual nuances, summarize the user's core pain points or satisfactions to generate a structured insight report. The goal is to help merchants quickly identify service shortcomings and optimize product experience, ensuring the analysis is objective, accurate, and provides actionable business guidance.
