How to Scrape and Analyze Maoyan Reviews for "Nezha" Using Python

1. Data Acquisition

We open the Maoyan page for "Nezha", switch to mobile view, and discover that the review list loads dynamically. By inspecting the XHR requests in the browser’s network panel we locate the JSON payload that contains the comments and download it.

2. Data Analysis

After crawling roughly 5,000 reviews we extract the following fields for each comment: gender (0 = unknown, 1 = male, 2 = female), user level, city, rating score, and the comment text itself.

3. Visualization

Using Python’s pyecharts library we create several visualizations to reveal patterns in the data:

Overall rating distribution – the film averages 9.7 points, with 86.5% of reviewers giving a perfect score.

Gender ratio – roughly a 1:1 split, with a slight female majority among those who disclose gender.

Top viewer cities – displayed both as a histogram of the ten most frequent cities and as an interactive map of the top eighty cities.

Word cloud of comment content – prominent terms include “good”, “awesome”, “effects”, as well as “story” and “plot”.

The analysis also notes that the film’s production spanned five years, involved over sixty script revisions, employed a team of more than a thousand people, and featured 1,800 shots with 1,300 special‑effects sequences, explaining its strong audience reception.