80 برومبت قوي لاستخراج رؤى عميقة من التعليقات باستخدام Google Gemini

نقدم لكم 80 برومبت قوي لاستخراج رؤى عميقة من التعليقات باستخدام Google Gemini
في عصر البيانات الضخمة، أصبحت تعليقات العملاء على المنتجات، الخدمات، المنشورات الاجتماعية، أو حتى المحتوى الرقمي كنزًا حقيقيًا لاستخراج رؤى قيمة. Google Gemini، بفضل قدراته المتقدمة في معالجة اللغة الطبيعية، يمكنه تحليل آلاف التعليقات بسرعة ودقة ليكشف عن المشاعر، الاتجاهات، الشكاوى، والاقتراحات.

في هذه المقالة، جمعتُ لك 80 برومبت جاهز للاستخدام مباشرة، مقسمة إلى 8 فئات (كل فئة 10 برومبتات). كل برومبت مكتوب بالإنجليزية لأفضل أداء، ويحتوي على placeholder مثل [paste comments here] حيث تلصق التعليقات. انسخ البرومبت، أضف تعليقاتك، واطلب من Gemini التحليل – ستحصل على رؤى احترافية فورًا.

1. تحليل المشاعر العام (General Sentiment Analysis)

1. Analyze the overall sentiment in these customer comments: [paste comments here]. Classify as positive, negative, neutral, or mixed, and provide percentages with key examples.
2. Perform sentiment analysis on the following comments: [paste comments here]. Break down sentiment by intensity (strong positive, mild positive, etc.) and highlight emotional triggers.
3. Summarize the dominant sentiment in these reviews: [paste comments here]. Provide a sentiment score from -10 (very negative) to +10 (very positive) with supporting quotes.
4. Detect sentiment trends in these social media comments: [paste comments here]. Identify any shifts from positive to negative or vice versa.
5. Analyze sentiment distribution across these comments: [paste comments here]. Show counts for positive, negative, and neutral, with representative examples for each.
6. Identify the most common emotions expressed in these comments: [paste comments here] (e.g., joy, frustration, excitement, disappointment).
7. Rate the overall customer satisfaction level based on these comments: [paste comments here], and explain the main drivers.
8. Compare sentiment in comments before and after a specific update: Group A [paste pre-update comments], Group B [paste post-update comments].
9. Extract sarcastic or ironic comments from this list: [paste comments here], and explain how they affect overall sentiment.
10. Provide a sentiment heatmap summary for these comments: [paste comments here], grouping by themes if possible.

2. استخراج المواضيع والكلمات المفتاحية (Theme & Keyword Extraction)

11. Extract the top 10 most frequently mentioned topics from these comments: [paste comments here], ranked by frequency.
12. Identify emerging themes and recurring keywords in these customer feedback comments: [paste comments here].
13. Cluster these comments into main themes: [paste comments here], and label each cluster with a short description.
14. List the most common positive and negative keywords/phrases in these reviews: [paste comments here].
15. Perform topic modeling on these comments: [paste comments here], and suggest 5–7 core topics with example quotes.
16. Extract product features most discussed in these comments: [paste comments here], with frequency counts.
17. Identify hidden or subtle themes not immediately obvious in these comments: [paste comments here].
18. Generate a word cloud description based on keyword frequency in these comments: [paste comments here].
19. Find connections between different topics mentioned across these comments: [paste comments here].
20. Extract trending hashtags or phrases from social media comments: [paste comments here].

3. تلخيص وتجميع التعليقات (Summarization & Clustering)

21. Summarize the key takeaways from these 100+ comments in bullet points: [paste comments here].
22. Provide a concise executive summary of customer opinions in these reviews: [paste comments here].
23. Group similar comments into categories and summarize each category: [paste comments here].
24. Create a one-paragraph overall summary of these comments: [paste comments here], highlighting consensus points.
25. Identify the top 5 most representative comments that capture the majority view: [paste comments here].
26. Summarize positive feedback separately from negative feedback in these comments: [paste comments here].
27. Generate a TL;DR version for each major theme in these long comment threads: [paste comments here].
28. Condense these verbose comments into key insights: [paste comments here].
29. Create a structured summary table: themes, sentiment, frequency, examples from these comments: [paste comments here].
30. Summarize user pain points and delights in separate sections: [paste comments here].

4. تحديد الإيجابيات والسلبيات (Strengths & Weaknesses)

31. List the top strengths mentioned by users in these comments: [paste comments here], ranked by frequency.
32. Identify the most common complaints or weaknesses in these reviews: [paste comments here].
33. Extract what customers love most about the product/service from these comments: [paste comments here].
34. Highlight critical issues or deal-breakers mentioned repeatedly: [paste comments here].
35. Compare praised features vs. criticized features in these comments: [paste comments here].
36. Find aspects that exceeded expectations according to these comments: [paste comments here].
37. List features users feel are missing or underdeveloped: [paste comments here].
38. Extract compliments about customer service from these comments: [paste comments here].
39. Identify quality-related positives and negatives: [paste comments here].
40. Summarize value-for-money perceptions from these comments: [paste comments here].

5. اقتراحات التحسين والشكاوى (Suggestions & Complaints)

41. Extract all user suggestions for improvement from these comments: [paste comments here], categorized.
42. List actionable recommendations made by customers: [paste comments here].
43. Identify the most urgent complaints that need immediate attention: [paste comments here].
44. Find feature requests mentioned in these comments: [paste comments here], ranked by popularity.
45. Summarize common workaround solutions users are sharing: [paste comments here].
46. Extract bug reports or technical issues described: [paste comments here].
47. Highlight usability complaints and suggested fixes: [paste comments here].
48. Identify pricing-related complaints and suggestions: [paste comments here].
49. Find suggestions for new content or features from these comments: [paste comments here].
50. Prioritize user-requested changes based on frequency and sentiment: [paste comments here].

6. رؤى حول ميزات محددة (Feature-Specific Insights)

51. Analyze comments specifically mentioning [feature name]: [paste comments here], and summarize opinions.
52. Extract feedback on design/aesthetics from these comments: [paste comments here].
53. Focus on performance-related comments: [paste comments here], positive and negative.
54. Summarize opinions about ease of use in these reviews: [paste comments here].
55. Analyze battery life or durability mentions: [paste comments here].
56. Extract feedback on integration with other tools/services: [paste comments here].
57. Find comments about security/privacy concerns: [paste comments here].
58. Summarize reactions to the pricing model: [paste comments here].
59. Analyze feedback on customer support experience: [paste comments here].
60. Extract opinions on specific update/version: [paste comments here].

7. تحليل الاتجاهات الزمنية والمقارنة (Temporal & Comparative Analysis)

61. Compare sentiment over time: older comments [paste set A] vs. recent comments [paste set B].
62. Identify how opinions have evolved based on comment dates: [paste comments with dates].
63. Spot emerging trends in recent comments vs. older ones: [paste comments here].
64. Compare feedback for Product A vs. Product B: Set A [paste A], Set B [paste B].
65. Analyze seasonal or event-related spikes in certain topics: [paste comments here].
66. Track how a specific issue’s mentions have changed over time: [paste comments here].
67. Identify if negative sentiment is decreasing after a fix: pre-fix [paste] vs. post-fix [paste].
68. Compare user satisfaction across different platforms: [paste platform-specific comments].
69. Find patterns in comment volume and sentiment correlation: [paste comments here].
70. Detect if competitor mentions are increasing: [paste comments here].

8. رؤى تسويقية وديموغرافية (Marketing & Demographic Insights)

71. Identify potential customer personas from these comments: [paste comments here].
72. Extract quotes suitable for testimonials or marketing: [paste comments here].
73. Find user-generated content ideas from enthusiastic comments: [paste comments here].
74. Analyze what drives word-of-mouth recommendations: [paste comments here].
75. Infer demographic clues (age, profession, etc.) from comment language: [paste comments here].
76. Identify brand loyalty signals and detractors: [paste comments here].
77. Extract emotional connection points users have with the brand: [paste comments here].
78. Find opportunities for social proof or case studies: [paste comments here].
79. Analyze competitor comparisons made by users: [paste comments here].
80. Suggest marketing messages based on positive themes in these comments: [paste comments here].

هذه البرومبتات تحول Gemini إلى محلل بيانات قوي للتعليقات. ابدأ بتجربة واحدة، ثم دمج النتائج لتقارير شاملة. يمكنك دائمًا إضافة “Provide results in a table format” أو “in Arabic” لتخصيص الإخراج.