From Comments to Trends: Uncovering User Needs and Sentiments with TikTok Comment Data

Introduction

In short video marketing, views and likes often present a superficial sense of success. However, the comment section is where the real user mindset is revealed.

Who is watching? How do they perceive the content? Do they like it or not? Are there complaints or feedback? — All these insights are hidden within the comment data.

This article focuses on analyzing TikTok comments using the high-dimensional comment API provided by LuckData, showing you step by step how to:

• Retrieve comments from target TikTok videos

• Analyze comment sentiments (positive/negative trends)

• Extract keywords to identify real user pain points and interests

• Support content creation, product selection, and brand reputation monitoring

1. Why Are Comments More Important Than Views?

For example, a product review video might have many likes, but if the comments are filled with phrases like “doesn’t work,” “poor quality,” or “another ad again,” it may suggest the product has low market acceptance.

Comments often reveal deeper insights and help identify areas for improvement or opportunities.

2. What Can LuckData’s TikTok Comment API Do?

LuckData offers a comment API that lets you retrieve the comment list and fields for any public TikTok video, making it easy to collect and analyze comment data.

• API Example:

GET https://luckdata.io/api/tiktok-api/get_comment_list?video_id=7216458890536592673&page=1

• Returned Fields Include:

  • Comment text

  • Timestamp

  • Commenter ID / username

  • Like count on each comment

  • Replies to comments (recursively expandable)

These fields can be further analyzed using natural language processing tools for keyword extraction, frequency analysis, sentiment classification, and topic modeling.

3. Quick Start: Use Python to Fetch and Analyze Keywords

Below is a simple Python example showing how to call the API and extract high-frequency keywords:

import requests
from collections import Counter
import re
headers = {"X-Luckdata-Api-Key": "your_luckdata_key"}
video_id = "7216458890536592673"
res = requests.get(
f"https://luckdata.io/api/tiktok-api/get_comment_list?video_id={video_id}&page=1",
headers=headers
)
comments = [c['text'] for c in res.json().get("data", [])]
# Extract Chinese keywords (basic approach: 2+ character sequences)
words = re.findall(r'[\u4e00-\u9fa5]{2,}', " ".join(comments))
top_words = Counter(words).most_common(10)
print("Top 10 Frequent Keywords:")
for word, count in top_words:
print(f"{word}: {count}")

For deeper analysis, you can use WordCloud for visualization, Jieba for precise Chinese word segmentation, and sentiment tools like SnowNLP, TextBlob, or VADER for sentiment classification.

4. The Value of Comment Data in Marketing

Comment data supports decision-making not only in marketing but also in product development, customer service, and community management.

5. Build a “Comment Sentiment Radar” Module

To integrate comment analysis into BI dashboards or visual platforms, consider these modules:

1. Keyword Cloud: Visualize the most frequent words to identify focus topics

2. Sentiment Distribution Chart: Show positive vs. negative comment ratios to assess public reception

3. Comment Trend Line: Track how comment volume changes over time to estimate content heat

4. Top Liked Comments List: Surface the most liked comments to identify mainstream opinions or highlights

These modules can be implemented using Dash, Streamlit, Tableau, Power BI, or other visualization tools.

6. Summary and Outlook

TikTok comments are not an afterthought — they are a high-value feedback pool.

With systematic extraction and analysis, you can:

• Hear the authentic voice of your users

• Accurately understand market preferences and content direction

• Proactively detect potential public opinion risks

• Identify opportunities and trends earlier than competitors

In future articles, we’ll explore how to combine comment data across platforms to build a unified social listening framework. Stay tuned.

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