Predicting Viral Products: How to Leverage Search Trends and Comment Sentiment to Identify Winning Products

Introduction

In the world of e-commerce operations and short video content planning, "viral products" are the engines that fuel explosive growth. However, not all products have the potential to go viral. Blindly following trends or relying on gut feeling can easily lead to poor product selection, inventory pileups, and wasted marketing resources.

So how do we identify potential winning products before they go viral?

This article dives deep into how to leverage LuckData’s multi-platform data (Douyin, TikTok, Walmart, Lazada) to assess product potential. By analyzing keyword search trends, user sentiment in comments, and category-wide momentum, you can build a systematic model to guide your product selection, content planning, advertising decisions, and inventory management with data-driven precision.

1. What Is the Core Logic Behind Predicting a Viral Product?

Viral products are rarely accidental—they are the result of multiple indicators aligning across dimensions:

✅ When several of these dimensions point in the same direction, it strongly indicates viral potential.

2. Viral Product Prediction Model: A 3-Step Data-Driven Strategy

Step 1: Analyze Keyword Trend Curves (Search Heat)

Search behavior reveals emerging demand. By monitoring keyword trends over time, you can predict product potential early. LuckData provides APIs to access search heat data from multiple platforms across time and regions.

import requests
headers = {
'X-Luckdata-Api-Key': 'your_luckdata_key'
}
response = requests.get(
'https://luckdata.io/api/douyin-API/get_xv5p?city=110000&type=rise_heat&start_date=20240501&end_date=20240507',
headers=headers,
)
print(response.json())

Tips:

• Track both core keywords and long-tail variations (e.g., “desktop fan”, “USB mini fan”, “quiet fan”)

• Observe whether growth is steady (organic spread) or sudden (possible viral spike)

• Use regional breakdowns (e.g., Tier 1 vs. Tier 3 cities) to localize opportunity mapping

Step 2: Analyze Related Product Videos and Comment Sentiment

Rising search interest isn't enough. You need to validate whether actual users are satisfied post-purchase. Sentiment analysis of comments reveals customer experience and product quality insights.

import requests
headers = {
'X-Luckdata-Api-Key': 'your_luckdata_key'
}
response = requests.get(
'https://luckdata.io/api/douyin-API/get_pa29?type=items,cnt,trends,author&item_id=7451571619450883355',
headers=headers,
)
print(response.json())

Key analyses include:

• Positive-to-negative comment ratio: Ratios above 3:1 suggest strong user satisfaction

• Keyword clustering: Identify high-frequency terms such as “quiet”, “portable”, “powerful”

• Risk flags: Look for common complaints like “overheats”, “poor battery”, “bad quality”

✅ These insights also help optimize product descriptions and content scripts.

Step 3: Assess Category-Wide Heat Distribution

Is this just one product gaining attention, or is the whole category trending? LuckData’s category APIs help you assess macro demand across platforms.

Lazada API Example:

import requests
headers = {
'X-Luckdata-Api-Key': 'your_luckdata_key'
}
response = requests.get(
'https://luckdata.io/api/lazada-online-api/gvqvkzpb7xzb?page=1&site=vn&query=storage box',
headers=headers,
)
print(response.json())

Walmart API Example:

import requests
headers = {
'X-Luckdata-Api-Key': 'your_luckdata_key'
}
response = requests.get(
'https://luckdata.io/api/walmart-API/get_hugc?page=1&keyword=storage box',
headers=headers,
)
print(response.json())

Use Cases:

• If “storage boxes” are trending on TikTok, Walmart, and Lazada simultaneously, the category itself is heating up

• Break down sub-categories (e.g., stackable, drawer-type, foldable) to spot specific winners

• Assess brand concentration to avoid over-saturated categories with entrenched players

3. Real Case: Can Fans Become a Viral Product?

Data-driven observations:

✅ Conclusion:

This product has seasonal demand, clear usage scenarios (e.g., office, dorm, car), strong video engagement potential, and positive reviews. It is ideal for summer campaigns, especially with tiered pricing (entry-level and premium models) and limited-time bundles to drive urgency and conversions.

4. How LuckData Helps Build a Predictive Viral Product System

LuckData provides a complete suite of data modules—from trend monitoring to sentiment analysis, video engagement tracking, and cross-platform comparison—enabling end-to-end predictive modeling.

✅ This system empowers product teams, content creators, and MCNs to make smarter, faster, and data-backed decisions at every stage.

5. Conclusion: Don’t Wait Until Others Go Viral

Teams with real competitive advantage spot trends before they explode.

✅ In an era of lightning-fast information, the winners are those who own the data.

Start using search trends and comment sentiment analysis today to identify and launch your next winning product. Data isn’t just support—it’s your strategic weapon.

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