• "What are people saying about X on Reddit/Bilibili/GitHub?"
• "Analyze the comments on this post"
• "What are the main points of disagreement here?"
• "Help me understand the community's attitude towards this issue"
• "Conduct opinion analysis on this topic"

1. Remove bot comments, spam, and meaningless comments (e.g., "+1", "bump", single emojis)
2. If there are more than 500 comments, perform strategic sampling — select high-like comments + randomly sample medium-popularity comments to capture minority viewpoints
3. Retain metadata (like count, author) — helps judge which viewpoints are more popular

comments_cleaned.json

• Read comments in batches (50-100 at a time) and perform the first round of grouping
• Merge clusters by comparing results across batches — same argument = same cluster
• Each cluster should represent a unique position or argument, not just a topic
• Name each cluster with a concise argument statement (instead of a hashtag)

• Good example: "This feature breaks backward compatibility and should be made optional"
• Bad example: "Backward compatibility concerns"

clusters.json

[
  {
    "cluster_id": 1,
    "name": "Concise argument statement",
    "comment_count": 45,
    "representative_comments": ["full text of 2-3 best examples"],
    "support_ratio": 0.7,
    "sample_comment_ids": ["id1", "id2", "id3"]
  }
]

1. Position: What exactly is this group arguing for?
2. Arguments: What facts, experiences, or logic do they cite?
3. Belief Strength: Are they definitive or hesitant? Use language cues and like counts to judge
4. Relationship with Other Clusters: Is this an opposing view to another cluster? Or a supplement or extension?

• Core Debate Axes: Fundamental disagreements (e.g., "Security vs. Convenience", "Innovation vs. Stability")
• Consensus Points: Points agreed upon by most clusters
• Disagreement Points: Points where the community has clear opposition
• Minority Viewpoints: Viewpoints held by few but with strong arguments

Data Source: [URL] Total Comments: N (M valid comments analyzed) Generated Time: YYYY-MM-DD

• Proportion: ~X% (about N comments)
• Core Arguments: [Main reasons supporting this viewpoint]
• Typical Comments: [1-2 representative original comments]
• Popularity: [Like count/support level]

• [Points agreed upon by most people]

• [Main opposing points, which viewpoints are in direct conflict]

• [Viewpoints held by few but with strong arguments, worthy of attention]

• Overall Sentiment: [Positive/Negative/Neutral/Mixed]
• Sentiment Intensity: [Intense/Mild]
• Sentiment Trend: [If timeline data is available]

If the user also requests JSON output, save the structured data as well.

• Don't just count votes — a high-like minority viewpoint may be more important than a low-participation majority position
• Pay attention to how people argue, not just what they say. Sarcasm, emotional language, and defensive statements all indicate strong positions
• Look for implied arguments — sometimes the real disagreement is not explicitly stated (e.g., people arguing about implementation details may actually be arguing about priorities)
• Cross-reference with replies — a reply strongly opposing a parent comment reveals the debate structure
• If comments involve multiple languages, cluster by argument (regardless of language), then note the language distribution in each cluster