This tutorial demonstrates how to use Python and Selenium to collect thousands of hot comments from QQ Music, parse the data from dynamically loaded list items, store it in CSV files, and finally generate a word‑cloud visualization of the comment content.
This tutorial walks you through building a Python project that downloads a Bilibili video, extracts its frames, applies Baidu AI human segmentation, scrapes danmu comments, generates a stylized word‑cloud animation, and finally composes a video with background music, showcasing video processing, AI, and data visualization techniques.
This guide demonstrates how to quickly generate TreeMind tree diagrams and word‑cloud visualizations using Wenxin Yiyan 4.5 by enabling the Call Tool option in X1, providing example prompts, and showing the resulting images, highlighting AI’s role as a helpful assistant.
This article presents a complete data‑analysis workflow that scrapes complaint records from a Chinese platform, cleans and merges the data, performs time‑series analysis, generates visualizations and word‑clouds, and interprets the findings to reveal complaint trends and user demands.
Learn step‑by‑step how to extract text from a CSV generated by a Python web crawler, clean it with stop‑words, create a word‑cloud visualization, compute word frequencies, and perform sentiment analysis using jieba and SnowNLP, with all code snippets provided.
This article demonstrates how to use Python to scrape Weibo comments about the TV series 'The Legend of the Year', generate word clouds, compute character frequencies and co‑occurrences, and visualize the resulting character relationship network with Gephi.
This article demonstrates how to scrape a news webpage with Python, extract and segment its Chinese text using jieba, count word frequencies, and visualize the top ten terms as a word cloud and a pie chart with pyecharts.
This guide walks you through extracting Jay Chou's lyrics from a Chinese lyrics JSON database, preprocessing the text with Python and Jieba, generating frequency tables, and visualizing the results as word clouds using both code and online tools.
This article walks through a Python word‑cloud issue where Chinese characters were not displayed, shows how to suppress irrelevant warnings, and provides the exact Matplotlib font settings and font‑installation steps needed to render the characters correctly.
This article walks through using Python to crawl Bilibili danmu (bullet comments), process the text with jieba, and generate a visual word cloud, providing complete code examples and tips for adapting the script to other videos.
This tutorial walks through building a simple Python web crawler for the QDaily website, covering target analysis, environment setup, SQLite database creation, data extraction with requests and BeautifulSoup, storing articles and comments, performing basic analysis, and visualizing results with a word cloud.
This article explains how Bilibili’s Chronos cross‑platform engine enables a new support‑bullet‑screen feature that turns user comments into animated word‑cloud overlays on video, detailing the client‑side layout algorithm, integral‑image optimization, spiral point generation, and worker‑thread rendering to achieve sub‑second performance on mobile devices.
This article presents a series of concise Python examples—each under ten lines—demonstrating how to generate QR codes, create word clouds, perform image segmentation, conduct sentiment analysis, detect masks, automate message sending, extract text with OCR, and build a basic number‑guessing game, showcasing the versatility of Python for quick prototyping across AI and utility tasks.
This guide walks you through analyzing Douyin's web pages, bypassing its anti‑scraping checks, constructing the proper API URLs, fetching comment data via Python, storing the results, and visualizing the comments as a dynamic dancing word cloud.
ByteDance’s data platform front‑end team surveyed academic, commercial, and open‑source word‑cloud solutions, identified gaps in geo‑ and shape‑based clouds, and engineered a performant front‑end layout algorithm that generates customizable shape word clouds for diverse business scenarios.
This article surveys academic, commercial, and open‑source word‑cloud solutions, explains the underlying algorithms, visual encodings, layout strategies, interaction techniques, and classifications, and discusses the strengths, limitations, and future directions of word‑cloud visualisation.
This guide walks you through extracting text from a CSV obtained via Python web scraping, cleaning it with stop‑words, generating a word‑cloud, performing jieba tokenization and frequency analysis, and finally applying SnowNLP for sentiment scoring, with all code snippets and data links provided.
This article walks through using Python to scrape Wang Leehom’s divorce‑related Weibo comments, clean the noisy dataset, visualize hourly comment trends, compare with his ex‑wife’s posts, generate word‑clouds and emoji frequency charts, and provides full code and data for reproducible analysis.
This tutorial demonstrates how to use Selenium to crawl short comments from a Douban movie page, extract them via XPath, paginate through results, and finally create a visual word cloud using Python's wordcloud library.
This tutorial walks you through creating a full‑stack word‑cloud web application using Flask for the backend and Vue for the frontend, covering environment setup, project structure, dependency installation, core code snippets, and how to run and package the app.
This tutorial walks through creating a word‑cloud web application using Flask for the backend and Vue for the frontend, covering environment setup, project structure, installing dependencies, writing API endpoints, building UI components, and packaging the app for deployment.
This tutorial demonstrates how to use Python to scrape Weibo for the most common National Day leave reasons, extract comment data via Ajax requests, store it in CSV files, and create visualizations such as top‑liked users and word‑clouds to reveal popular motifs.
This article walks through a real‑world Python project that uses Selenium to crawl the first ten pages of Taobao snack listings, extracts sales, price and location data, visualizes price distribution and geographic concentration, generates a word‑cloud of top user comments, and lists the top‑selling stores, providing full source code for replication.
This article presents a Python‑based web‑scraping project that collects the first ten pages of "小鱼零食" search results on Taobao, analyzes sales, price distribution, geographic store locations, top comments, and visualizes the findings with bar charts and word clouds.
This tutorial walks through collecting complaint data from the Black Cat platform during the pandemic, cleaning and merging the datasets with pandas, analyzing temporal trends, and generating insightful word clouds using jieba and WordCloud, all illustrated with Python code and visualizations.
This tutorial demonstrates a complete Python data‑analysis workflow that scrapes complaint records for a rental‑apartment company, cleans and merges the data, visualizes complaint trends, and generates word‑clouds to reveal common issues and user demands.
Using Python, the author collected and visualized Douban and Weibo comments on the film “Chinese Doctor,” showing rating distributions, keyword word clouds, and fan sentiment, and compares the 2021 movie with the 2019 documentary to illustrate audience perception of the pandemic drama.
This tutorial walks you through creating a word‑cloud generator with a Vue front‑end and a Flask back‑end, covering environment setup, project scaffolding, component development, API integration, and deployment, all illustrated with code snippets and screenshots.
This tutorial shows how to create a multithreaded Python web scraper that fetches news from Sina and NetEase, stores the results in MySQL, generates a live word‑cloud, and serves the data through a simple Flask web application with an HTML front‑end.
This article walks through the complete process of designing a Python web scraper to collect Zhihu answer data, parse author, ID and excerpt fields, store the results in CSV files, and finally visualize the text with a word‑cloud, including all necessary code snippets and explanations.
This guide walks through generating word clouds from JD product reviews using Python, covering preprocessing, tokenization, frequency analysis, and visual creation with the stylecloud, wordcloud, and pyecharts libraries, including mask image selection, palette customization, and full code snippets for each method.
This article demonstrates how to use Python to crawl over 200,000 Bilibili comments, analyze popular memes across different content categories, and presents a step‑by‑step guide to extracting danmu via network requests with code examples for practical data mining.
The article demonstrates how to scrape over 200,000 iQiyi danmu comments for the drama “The Bad Kids” using Python, then analyzes user activity, episode popularity, top comments, actor mentions, and visualizes the results with word clouds and charts.
This article demonstrates how to scrape bullet comments from iQiyi for the first episode of a popular mystery series, decode the binary files, extract the text, and use Python's jieba and wordcloud libraries to clean the data and generate a visual word cloud of audience sentiments.
The article details how a data analyst scraped comments from Zhihu, Weibo, and Bilibili about the 2020 Youth Day video “后浪”, applied Baidu AI sentiment analysis and Python word‑cloud generation, and compared the differing emotional tones across platforms.
Analyzing eleven years of Stack Overflow data for the eleven most popular programming languages, this article uncovers the most frequent questions, dominant frameworks, libraries and data types that trouble developers, showing how language popularity and pain points have shifted over time.
This tutorial walks you through analyzing JD product pages, extracting comment data via requests with proper headers, handling pagination, saving results, cleaning text using jieba, and visualizing frequent terms as a word cloud, all illustrated with step‑by‑step screenshots and code snippets.
This tutorial walks through extracting Python job postings from Zhaopin, storing them in MongoDB, cleaning the data with pandas, visualizing national and city‑level salary distributions, and generating word clouds of required skills using matplotlib and wordcloud, providing a complete end‑to‑end data analysis pipeline.
This article demonstrates how to extract 1,000 short movie reviews stored in MongoDB, apply Chinese word segmentation using Jieba, select the top 50 terms, generate a visual word cloud, and perform additional analyses such as top‑liked comments and 15‑day comment volume trends.
This article explains how to build a Python tool that monitors real‑time WeChat chats, uses wxpy to capture messages, applies jieba for Chinese word segmentation, and generates a heart‑shaped word cloud with the wordcloud library, complete with code examples and setup instructions.
This tutorial walks you through extracting WeChat Moments with a Python web scraper, processing the Chinese text using jieba, and visualizing the most frequent words as beautiful word clouds with customizable shapes and fonts.
Using Python web scraping, jieba segmentation, and word‑cloud visualization, this article analyzes Douban comments for the three iconic anime series One Piece, Naruto, and Bleach, highlighting dominant themes, character popularity, and audience sentiment through keyword frequency maps.
This article demonstrates how to use the Python itchat library to extract personal WeChat friend data, then analyzes gender ratios, city distribution, and signature word clouds with R and Python visualisation tools, offering a practical guide for personal social network analytics.
This article scrapes over 22,000 Douban comments for the TV series “The Hunt,” cleans the data, applies SnowNLP‑based sentiment classification and word‑cloud visualization, and shows that while overall ratings are mixed, detailed short reviews are overwhelmingly positive.
This tutorial walks through analyzing Python job postings scraped from Zhaopin, covering data extraction from MongoDB, cleaning, salary distribution analysis, top city rankings, and generating word clouds of required skills, with complete Python code and visualizations.
This tutorial demonstrates how to capture hot comments from NetEase Cloud Music using web‑scraping techniques, handle the platform's encrypted API, and generate a Chinese word cloud with Python's WordCloud library for visual insight.
