Build a Python Web Scraper to Extract Taobao Product Reviews

Project Overview

The goal is to collect Taobao product reviews, identify frequently mentioned features such as waterproof, large capacity, and aesthetics, and summarize customer preferences.

Preparation

1. Install Python and PyCharm. Follow a detailed tutorial on setting up the Python environment.

2. Obtain the product page URL, for example:

https://detail.tmall.com/item.htm?spm=a230r.1.14.1.55a84b1721XG00&id=552918017887&ns=1&abbucket=17

3. Install required libraries (requests, beautifulsoup4, simplejson, etc.) via PyCharm's Project Interpreter settings.

Implementation

1. Import the necessary libraries:

import requests</code><code>from bs4 import BeautifulSoup as bs</code><code>import json</code><code>import csv</code><code>import re

2. Use Chrome DevTools (Network tab) to locate the list_detail_rate.htm request that returns review data.

3. Define a variable to store the page URLs: PAGE_URL = [] 4. Create a function that generates the list of review page URLs by concatenating strings.

5. Build a function to fetch and parse review data, extracting fields such as username, review time, color, and comment. The required cookie can be copied from the Network tab.

6. Parse the JavaScript response and write the extracted data to a text file.

7. Define a main function to iterate over the desired number of review pages and invoke the data‑extraction routine.

The final output shows the collected reviews.

Summary

1. Using a Python web scraper, we successfully harvested Taobao product reviews; the method works but should be used responsibly to avoid excessive server load.

2. To obtain the full source code, reply with “淘宝评论” to the associated WeChat public account.