Scrape NetEase Cloud Music Hot Comments and Visualize Them with Word Clouds

Introduction

Web crawlers enable many fun applications, such as extracting comments, while word clouds visualize word frequency in an appealing graphic. This article combines both techniques to scrape NetEase Cloud Music hot comments and create a word cloud.

Data Capture

To build a word cloud, we first need comment data, which requires basic crawling skills:

Packet capture analysis

Encrypted information handling

Fetching hot comment content

Packet Capture

Using Chrome DevTools, locate the comment API URL (see image). The request is a POST with specific fields.

Encrypted Information Handling

By copying the request payload from the browser, we can bypass encryption for this tutorial. For a deeper understanding, refer to community analyses of NetEase Cloud Music's encryption.

NetEase Cloud Music New Login API Analysis

Fetching Hot Comments

The API returns a JSON string containing the comments, which can be parsed directly.

Running the script yields the comment data (see result image).

Word Cloud Generation

The WordCloud library (installable via pip) is used to create the visualization. The library’s documentation provides detailed examples.

Chinese characters require specifying a font file to avoid garbled output. The configuration is:

WordCloud(random_state=1, font_path=r'C:/Users/Windows/fonts/simkai.ttf')

Result

The final word cloud visualizes the most frequent terms in the hot comments.

Conclusion

This article demonstrated how to scrape NetEase Cloud Music hot comments and generate a corresponding word cloud. Possible extensions include using mask images for shaped word clouds, batch scraping multiple songs by ID, and packaging the functionality as a comment‑or‑word‑cloud service.