Generating Word Cloud and Pie Chart from a News Article Using Python

The article shows a step‑by‑step method to process a news article and create visual representations of the most frequent words.

Solution steps: 1) Crawl all text from the news page; 2) Split the text into words; 3) Count each word’s occurrences and keep the top 10; 4) Generate a word cloud and a pie chart.

Code example:

import jieba as jieba
import requests
from bs4 import BeautifulSoup
from pyecharts.charts import WordCloud, Pie

if __name__ == "__main__":
    headers = {
        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36"
    }
    url = "https://new.qq.com/rain/a/20230315A08LAK00"
    res_html = requests.get(url, headers=headers).text
    soup = BeautifulSoup(res_html, "lxml")
    txt = soup.select(".content-article")[0].text
    words = jieba.lcut(txt)
    counts = {}
    for word in words:
        if len(word) == 1:
            continue
        else:
            counts[word] = counts.get(word, 0) + 1
    sort_data = sorted(counts.items(), key=lambda a: a[1], reverse=True)[:10]
    wc = WordCloud()
    wc.add("", sort_data, word_size_range=[20, 100])
    wc.render("1.html")
    pip = Pie()
    pip.add(series_name="次数", data_pair=sort_data)
    pip.render("2.html")

The resulting word cloud and pie chart display the most frequent words extracted from the article, providing a quick visual insight into its content.