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Анализ тональности отзывов

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Глубокий анализ эмоциональной тенденции и извлечение ключевых элементов отзывов пользователей на платформах электронной коммерции для помощи продавцам в оптимизации продуктов и услуг.

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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.

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Выступайте в качестве старшего аналитика данных электронной коммерции, чтобы выполнить глубокий анализ определения полярности эмоций и извлечение ключевых элементов в предоставленном тексте отзывов пользователей. Во-первых, определите эмоциональную полярность (положительную, отрицательную или нейтральную), выраженную в отзыве, и количественно оцените ее интенсивность. Во-вторых, точно извлеките конкретные точки зрения, касающиеся функций продукта, логистического опыта и послепродажного обслуживания, из текста. Наконец, объедините контекст, чтобы определить, есть ли ирония или скрытое недовольство, и выведите структурированный отчет об эмоциональном анализе, чтобы помочь продавцам быстро определить болевые точки обслуживания и преимущества продукта, не используя никаких структурированных шаблонов или нагромождений повелительных глаголов, непосредственно излагая процесс анализа и выводы естественным языком.

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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.
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Ответ на отзыв в электронной коммерции
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Анализ тональности отзывов
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