English 
Comments Are Gold: Unlocking Risks and Potential Through User Feedback
Introduction
Sales data tells you how well something is selling, but comment data tells you why it’s selling (or not).
In the age of short video platforms and e-commerce, user comments carry enormous signal value:
When people praise the “luxurious packaging,” it reflects strong brand perception.
Repeated mentions of “pungent smell” indicate a potential return risk.
Comments like “much smaller than shown in the video” can spark negative buzz.
In this article, we’ll walk you through how to build a multi-platform sentiment monitoring system based on Douyin + TikTok + e-commerce review data, helping you:
Detect hidden product risks and category pitfalls
Quickly identify “hidden selling points” loved by users
Reverse-engineer feedback to guide product optimization or marketing strategy
I. Why Comment Data Matters More Than Sales Numbers
Data Type | Advantages | Limitations |
|---|---|---|
Sales Data | Direct evaluation of sales performance | Cannot explain the reason |
Comment Data | Captures real user sentiment and insights | Scattered, unstructured, harder to process |
Comments are voluntary, organic expressions by users—making them some of the most authentic reflections of purchase motivations and user experience.
II. What Platforms Can We Extract Comments From?
With LuckData’s API, you can efficiently retrieve review content from the following platforms:
TikTok Comment API
comment list by video: Retrieve all comments by video IDreply list by commentid: View replies to top-level commentsSupports pagination, keyword filtering, and time-based queries
Douyin Comment API
/get_pa29includes comment counts and engagement metrics in video details (with the option to retrieve full comment data)
E-commerce Platforms (Walmart, Lazada, Amazon)
Fetch product reviews by item page or SKU ID, including text reviews, star ratings, timestamps, etc.
These data sources provide a foundation for cross-platform sentiment monitoring and product insight extraction.
III. Step-by-Step: Building a "Comment Sentiment Dashboard"
Step 1: Retrieve Comments for Target Video or Product
Using LuckData’s API, pull in raw user feedback.
# Example: Retrieve TikTok video commentsurl = "https://luckdata.io/api/tiktok-api/comment list by video?video_id=7253938482002"
The API returns fields such as: comment text, like count, timestamp, commenter username, etc.
Step 2: Natural Language Processing – Sentiment Scoring & Keyword Extraction
Run sentiment analysis to assign a sentiment score to each comment (range: -1 to 1), and extract key terms or themes.
Comment | Sentiment Score | Extracted Keywords |
|---|---|---|
“This works so well! Bought it three times!” | 0.92 (positive) | repurchase, effective |
“Packaging was damaged, customer service ignored me” | -0.84 (negative) | packaging, support |
“Way worse than shown in the video” | -0.75 (negative) | video, disappointment |
Tools like TextBlob, SnowNLP, Baidu NLP, or LuckData’s own templates can handle this step efficiently.
Step 3: Generate a Visual Sentiment Dashboard
Use visualization tools to present insights clearly and track trends over time:
Sentiment trend chart: Monitor shifts in positive vs. negative comment ratios
Negative keyword cloud: Identify recurring user frustrations
Positive keyword list: Highlight top product qualities that resonate with users
Comment volume & interaction trends: Detect viral moments and engagement peaks
This dashboard becomes an essential tool for ongoing brand health monitoring.
IV. Real-World Example: Crisis Detection for a Viral Product
A TikTok video promoting “Magic Foam Cleaner” hit 10 million views, earned 800K likes, and drove a rapid surge in sales.
However, in the comments, terms like “ineffective,” “pungent smell,” and “caused allergic reactions” kept appearing.
The system detected:
41% of comments were negative
High-frequency negative keywords included “smell,” “doesn’t work,” and “rash”
Brand responded quickly by revising product instructions and updating ad copy, averting a potential PR crisis
V. What Else Can You Do With Comment Mining?
Beyond crisis management, comment data enables a wide range of applications:
Identify most-mentioned feature keywords to understand product strengths
Extract high-quality UGC comments to repurpose as ad copy
Use like counts to spot comment “virality”, indicating sentiment trends
Track emotional spikes to detect emerging issues early
Comments are more than noise—they’re real-time product diagnostics.
VI. Why Choose LuckData for Comment Analysis?
Comparison Dimension | LuckData Comment API | Self-Built Scrapers |
|---|---|---|
Data Compliance | ✅ Authorized, compliant access | ❌ Risk of IP blocks, legal issues |
Real-Time Access | ✅ Fast, real-time updates | ❌ Slower, needs custom schedulers |
Multi-Platform Support | ✅ Covers multiple platforms | ❌ Usually single-platform only |
Technical Barrier | ✅ No-code templates, plug-and-play | ❌ Requires setup, dev, maintenance |
LuckData goes beyond data access—it offers structured pipelines and analysis-ready integrations for non-technical teams.
VII. Conclusion: Comments Are an Untapped Strategic Resource
In marketing, the biggest danger isn’t poor sales. It’s:
“We have no idea why users aren’t buying.”
By analyzing reviews from TikTok, Douyin, and major e-commerce platforms, brands can finally decode the language of their customers—understanding what works, what doesn’t, and how to act before problems grow.
Comments aren’t just reflections—they’re your roadmap to smarter decisions.
