apify-brand-reputation-monitoring
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Translation
ChineseBrand Reputation Monitoring
品牌声誉监控
Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors.
使用Apify Actors从多个平台抓取评论、评分以及品牌提及内容。
Prerequisites
前提条件
(No need to check it upfront)
- file with
.envAPIFY_TOKEN - Node.js 20.6+ (for native support)
--env-file - CLI tool:
mcpcnpm install -g @apify/mcpc
（无需提前检查）
- 包含的
APIFY_TOKEN文件.env - Node.js 20.6+（支持原生功能）
--env-file - CLI工具：
mcpcnpm install -g @apify/mcpc
Workflow
工作流程
Copy this checklist and track progress:
Task Progress:
- [ ] Step 1: Determine data source (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the monitoring script
- [ ] Step 5: Summarize results复制此检查表并跟踪进度：
Task Progress:
- [ ] Step 1: Determine data source (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the monitoring script
- [ ] Step 5: Summarize resultsStep 1: Determine Data Source
步骤1：确定数据源
Select the appropriate Actor based on user needs:
| User Need | Actor ID | Best For |
|---|---|---|
| Google Maps reviews | | Business reviews, ratings |
| Google Maps review export | | Dedicated review scraping |
| Booking.com hotels | | Hotel data, scores |
| Booking.com reviews | | Detailed hotel reviews |
| TripAdvisor reviews | | Attraction/restaurant reviews |
| Facebook reviews | | Page reviews |
| Facebook comments | | Post comment monitoring |
| Facebook page metrics | | Page ratings overview |
| Facebook reactions | | Reaction type analysis |
| Instagram comments | | Comment sentiment |
| Instagram hashtags | | Brand hashtag monitoring |
| Instagram search | | Brand mention discovery |
| Instagram tagged posts | | Brand tag tracking |
| Instagram export | | Bulk comment export |
| Instagram comprehensive | | Full Instagram monitoring |
| Instagram API | | API-based monitoring |
| YouTube comments | | Video comment sentiment |
| TikTok comments | | TikTok sentiment |
根据用户需求选择合适的Actor：
| 用户需求 | Actor ID | 最佳适用场景 |
|---|---|---|
| Google Maps评论 | | 商家评论、评分 |
| Google Maps评论导出 | | 专用评论抓取 |
| Booking.com酒店数据 | | 酒店数据、评分 |
| Booking.com评论 | | 详细酒店评论 |
| TripAdvisor评论 | | 景点/餐厅评论 |
| Facebook评论 | | 页面评论 |
| Facebook帖子评论 | | 帖子评论监控 |
| Facebook主页指标 | | 主页评分概览 |
| Facebook互动反应 | | 反应类型分析 |
| Instagram评论 | | 评论情感分析 |
| Instagram话题标签 | | 品牌话题标签监控 |
| Instagram搜索 | | 品牌提及发现 |
| Instagram标记帖子 | | 品牌标记跟踪 |
| Instagram导出 | | 批量评论导出 |
| Instagram全面监控 | | 完整Instagram监控 |
| Instagram API | | 基于API的监控 |
| YouTube评论 | | 视频评论情感分析 |
| TikTok评论 | | TikTok情感分析 |
Step 2: Fetch Actor Schema
步骤2：获取Actor Schema
Fetch the Actor's input schema and details dynamically using mcpc:
bash
export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"Replace with the selected Actor (e.g., ).
ACTOR_IDcompass/crawler-google-placesThis returns:
- Actor description and README
- Required and optional input parameters
- Output fields (if available)
使用mcpc动态获取Actor的输入模式和详细信息：
bash
export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"将替换为所选的Actor（例如）。
ACTOR_IDcompass/crawler-google-places此命令返回：
- Actor描述和README
- 必填和可选输入参数
- 输出字段（如果可用）
Step 3: Ask User Preferences
步骤3：询问用户偏好
Before running, ask:
- Output format:
- Quick answer - Display top few results in chat (no file saved)
- CSV - Full export with all fields
- JSON - Full export in JSON format
- Number of results: Based on character of use case
运行前，询问用户：
- 输出格式：
- 快速回答 - 在聊天中显示前几条结果（不保存文件）
- CSV - 包含所有字段的完整导出
- JSON - JSON格式的完整导出
- 结果数量：根据使用场景的特点确定
Step 4: Run the Script
步骤4：运行脚本
Quick answer (display in chat, no file):
bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT'CSV:
bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT' \
--output YYYY-MM-DD_OUTPUT_FILE.csv \
--format csvJSON:
bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT' \
--output YYYY-MM-DD_OUTPUT_FILE.json \
--format json快速回答（在聊天中显示，不保存文件）：
bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT'CSV格式：
bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT' \
--output YYYY-MM-DD_OUTPUT_FILE.csv \
--format csvJSON格式：
bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT' \
--output YYYY-MM-DD_OUTPUT_FILE.json \
--format jsonStep 5: Summarize Results
步骤5：总结结果
After completion, report:
- Number of reviews/mentions found
- File location and name
- Key fields available
- Suggested next steps (sentiment analysis, filtering)
完成后，报告以下内容：
- 找到的评论/提及数量
- 文件位置和名称
- 可用的关键字段
- 建议的后续步骤（情感分析、筛选）
Error Handling
错误处理
APIFY_TOKEN not found.envAPIFY_TOKEN=your_tokenmcpc not foundnpm install -g @apify/mcpcActor not foundRun FAILEDTimeout--timeoutAPIFY_TOKEN not foundAPIFY_TOKEN=your_token.envmcpc not foundnpm install -g @apify/mcpcActor not foundRun FAILEDTimeout--timeout