Analyze batches of Chinese comments to summarize positive, neutral, and negative sentiment, recurring words and phrases, and requests, complaints, or questions. Get counts and representative examples to support content reviews and future topic planning.
评论区量化分析：对一批评论做情感分析（正/中/负占比 + 代表评论）、高频词与短语提取、 以及需求/吐槽/提问的诉求挖掘，为内容复盘和选题反哺提供数据。当用户说"评论情感分析" "评论区分析""用户在说什么""评论正负面比例""评论高频词""评论关键词""口碑分析""评论词云" "用户诉求""评论区吐槽"时使用。
基于 jieba（分词）+ SnowNLP（情感）+ 社媒情感词典。
Review the instructions the agent will receive before you install.
对评论做量化洞察：情感分布、高频词/短语、需求与吐槽挖掘。走
scripts/comment_insights.py（jieba + SnowNLP + 社媒情感词典）。
和 skill-community-ops 的分工：community-ops 是"怎么回评论 + 危机应对"（运营动作）； 本 SKILL 是"评论区在说什么"（量化数据）。评论抓取见 skill-xhs-analyzer。
pip install jieba snownlp（首次 jieba 会构建词典缓存）。
评论数据，三种格式：
.txt：每行一条评论.json：字符串数组，或对象数组（--column 指定字段，默认 content）.csv：--column 指定评论列（默认首列/content）outputs/主题名/）脚本路径（相对项目根）：skills/builtin/skill-comment-insights/scripts/comment_insights.py。
python <skill>/scripts/comment_insights.py analyze -i comments.txt --top 20 \
-o outputs/主题名/report.json
# CSV 指定列
python <skill>/scripts/comment_insights.py analyze -i comments.csv --column 评论内容
沿用社媒评论分析常用组合：jieba 中文分词（#131 高频词）+ SnowNLP 情感（#130），并叠加 社媒情感词典（绝绝子/yyds/避雷/翻车等网络用语）修正 SnowNLP 在社交文本上的偏差。诉求挖掘 用规则匹配（求购/疑问/吐槽），确定可复现。
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