Sentiment Monitor — 通用舆情监控系统

Overview

A universal Chinese public opinion monitoring system. Supports any company, brand, or person as the monitoring target. Features four-level alert system, SnowNLP local sentiment classification (zero token), LLM precision judgment for uncertain items, semantic deduplication, heat scoring, and trend tracking.

All target-specific data (keywords, recipients, etc.) is driven by a JSON config file — no hardcoding.

Quick Start

Step 1: Initialize (First Time Only)

When the user triggers this skill for the first time, collect the following information:

1. Target name — What to monitor (e.g., "呈诺再生医学", "Apple中国", "华为")
2. Target aliases — English names, abbreviations (e.g., ["ALLIFE", "ALLIFE MEDICINE"])
3. Keywords — Ask user to provide keywords in 5 categories:
   - Core (核心): Main brand/company names (required, at least 1)
   - Products (产品): Product or service names (optional)
   - Tech (技术): Technical terms, patents, certifications (optional)
   - People (人物): Key people, partnerships, related companies (optional)
   - Variants (变体): Common misspellings, alternative names (optional)
4. Email recipient — Who receives the report
5. Schedule — Report frequency and times (default: daily 08:00, 12:00, 17:00)
6. Platforms — Search sources (default: all platforms)
7. Data directory — Where to store history/trends data (default: sentiment_data/ under workspace)

Step 2: Generate Config File

Generate a monitor_config.json in the user's workspace using this template:

{
  "target_name": "目标名称",
  "target_aliases": ["别名1", "别名2"],
  "keywords": {
    "core": ["核心关键词1", "核心关键词2"],
    "products": [],
    "tech": [],
    "people": [],
    "variants": []
  },
  "email_to": "recipient@example.com",
  "platforms": [],
  "data_dir": "sentiment_data",
  "created_at": "2026-04-18T00:00:00"
}

Use the generate_config() function from scripts/sentiment_engine.py to create this programmatically, or write it directly.

Step 3: Install Dependencies

pip install snownlp agentmail-sdk

SnowNLP (~7.7MB) provides local Chinese sentiment classification at 280 items/sec with zero token cost. AgentMail SDK is needed only if email delivery is required.

Step 4: Create Automation

Use automation_update tool to create scheduled report tasks. Each task's prompt should follow the Report Workflow below.

Report Workflow (for Automation Prompts)

Each scheduled report execution follows this workflow. Embed this in the automation prompt:

Phase 1: Import Engine

import sys
sys.path.insert(0, r"<SKILL_SCRIPTS_DIR>")
# <SKILL_SCRIPTS_DIR> is the absolute path to this skill's scripts/ directory
# On Windows: C:\Users\<USER>\.workbuddy\skills\sentiment-monitor\scripts
from sentiment_engine import *
from report_generator import make_html_report, make_text_report

Phase 2: Load Config & History

cfg = load_config("<WORKSPACE>/monitor_config.json")
history = load_json(get_history_file(cfg), {"items": []})

Phase 3: Multi-Platform Search

Read keywords from config:

keywords = get_all_keywords(cfg)
kw_counts = get_keyword_count(cfg)

Search all keywords across web, news, and social media platforms. Structure each result as:

{
    "title": "标题",
    "summary": "摘要",
    "source": "来源平台/媒体",
    "url": "链接",
    "time": "发布时间",
}

Phase 4: Deduplicate

all_items = deduplicate_items(raw_items)

Phase 5: Sentiment Classification (Token-Saving Strategy)

Apply the three-layer classification strategy:

Layer 1 — SnowNLP Local Classification (zero token)

results, uncertain_indices = batch_snow_classify(all_items)

• score > 0.70 → positive (confident, no LLM needed)
• score < 0.30 → negative (confident, no LLM needed)
• 0.30 ~ 0.70 → uncertain, needs LLM

Layer 2 — LLM Precision Judgment (only for uncertain items)
Send only items at uncertain_indices to LLM for classification. This typically covers 20-40% of total items, saving 60-80% of tokens.

Layer 3 — Alert Keyword Check (zero token, pure keyword matching)
For all items classified as negative, run assess_alert_level() to determine alert level:
- Red: 热搜/封杀/停业/吊销/刑拘/逮捕
- Orange: 处罚/违法/欺诈/造假/败诉/监管/调查/立案
- Yellow: 2+ same-topic negatives or heat >= 6
- Blue: default for other negatives

Phase 6: Heat Scoring

for i, item in enumerate(all_items):
    item["heat_score"] = calc_heat_score(item.get("source"), i // len(all_items) * 10 + 1, item.get("time"))
    item["sentiment"] = results[i]["sentiment"]
    item["alert_level"] = assess_alert_level(item, all_items)

Phase 7: Incremental Comparison

Compare with history.json to find new items. Get trend data:

trend = get_trend_summary(cfg)
since_label = time_ago_str(history.get("last_report_time"))

Phase 8: Generate Report

kw_summary = f"{kw_counts['core']}核心 + {kw_counts['total'] - kw_counts['core']}关联"
highest = get_highest_alert([i.get("alert_level") for i in new_items])

html = make_html_report(
    target_name=cfg["target_name"],
    pos_items=positive_items,
    neg_items=negative_items,
    neu_items=neutral_items,
    highest_alert=highest,
    trend_data=trend,
    summary_html=summary_html,
    run_label=get_run_label(),
    since_label=since_label,
    keyword_summary=kw_summary,
    snownlp_count=snownlp_classified_count,
    llm_count=llm_classified_count,
)

text = make_text_report(
    target_name=cfg["target_name"],
    # ... same args ...
    summary_text=summary_text,
)

Phase 9: Send Email

from report_sender import send_sentiment_report
send_sentiment_report(
    target_name=cfg["target_name"],
    run_label=get_run_label(),
    html_content=html,
    text_content=text,
    to_email=cfg["email_to"],
)

Phase 10: Update Data Files

record_trend(cfg, pos_count, neg_count, neu_count, highest, get_run_label())
# Also update history.json with new items and last_report_time

Automation Prompt Template

Copy and customize this template for each scheduled report:

执行 {target_name} 舆情监控任务（{早报/午报/晚报}），按以下步骤执行：

## 0. 导入引擎
import sys; sys.path.insert(0, r"C:\Users\{USER}\.workbuddy\skills\sentiment-monitor\scripts")
from sentiment_engine import *
from report_generator import make_html_report, make_text_report

## 1. 加载配置
读取 {WORKSPACE}/monitor_config.json 和历史数据

## 2. 全网搜索（{keyword_count}个关键词）
核心：{列出核心关键词}
产品：{列出产品关键词}
技术：{列出技术关键词}
关联：{列出关联关键词}
变体：{列出变体关键词}

## 3. 智能去重
使用 deduplicate_items() 去重

## 4. 情感分类（省token策略）
第一步：SnowNLP 本地初筛（零token）
第二步：仅不确定条目调 LLM 精判
第三步：预警关键词兜底（零token）

## 5. 增量对比 + 热度评分 + 预警评估

## 6. 生成 HTML+纯文本 报告

## 7. 发送邮件至 {email_to}

## 8. 更新数据文件（history.json + trends.json）

Manual Operations

Single Classification Test

from sentiment_engine import snownlp_classify
sentiment, confidence, needs_llm = snownlp_classify("文本内容")

CLI Commands

python sentiment_engine.py <config.json> keywords       # 列出关键词
python sentiment_engine.py <config.json> alerts         # 查看预警配置
python sentiment_engine.py <config.json> trends         # 查看趋势
python sentiment_engine.py <config.json> classify <文本>  # 测试分类
python sentiment_engine.py <config.json> benchmark      # 基准测试

Architecture

For detailed architecture and design decisions, read references/architecture.md.

Scripts

Dependencies