This article demonstrates how to use Python to crawl 7,275 Douban short reviews of the film “Letter to Grandma”, clean the data, generate a word‑cloud and frequency bar chart, and perform sentiment analysis that reveals over 91% of comments are positive.
This article guides Java developers through a complete Chinese sentiment‑analysis dataset exploration, covering label distribution, sentence length statistics, vocabulary counts, adjective extraction, and visual word‑cloud generation using Python libraries such as pandas, seaborn, jieba, and wordcloud.
This tutorial demonstrates how to convert a JSON lyric database into Excel, filter Jay Chou songs, perform Chinese word segmentation with Jieba, compute word frequencies, and create visualizations such as word clouds using Python code and online tools.
This tutorial demonstrates how to use Python libraries such as itchat, jieba, matplotlib, snownlp, and Tencent Youtu SDK to collect WeChat friend information and perform data analysis on gender distribution, avatar characteristics, signature sentiment, and geographic location, presenting the results with charts and word clouds.
This tutorial demonstrates how to preprocess a Chinese lyrics JSON dataset, extract Jay Chou's songs using Python, perform word segmentation with Jieba, compute word frequencies, and create visualizations such as word clouds both programmatically and with online tools.
This tutorial explains how to scrape a news article with Python, segment Chinese text, count word frequencies, and visualize the top ten words using a word cloud and a pie chart, providing complete code and sample results.
This tutorial walks through downloading a dance video, extracting frames, using Baidu AI for person segmentation, generating word‑cloud masks, and stitching the results into a dancing word‑cloud video with Python, OpenCV and the WordCloud library.
This tutorial walks you through collecting movie information from Maoyan using Python web‑scraping, storing the results in CSV, and then applying pandas, matplotlib, and WordCloud to analyze and visualize trends such as release years, genres, regions, durations, and ratings across China and the world.
This tutorial walks through a complete Python workflow that downloads a B‑site video, extracts frames with OpenCV, uses Baidu AI for human segmentation, crawls danmu comments, creates a masked word‑cloud animation, and finally merges the clips with audio into a polished video.
This tutorial walks you through using Python Selenium to automate QQ Music, scroll through the infinite‑scroll comment section, extract user avatars, names, timestamps and comment text, save the data to CSV, and finally visualize the comments with a word‑cloud image.
This tutorial walks through using Python's Selenium to automate scrolling and extract QQ Music hot comment data—including user avatars, names, timestamps, and content—then saves the information to CSV and creates a Chinese word cloud for visual analysis.
Learn how to install the Python wordcloud module, generate custom word clouds from text, customize fonts, colors, backgrounds, and stopwords, and integrate with Pillow and matplotlib, all illustrated with code snippets and images, to visualize text data effectively.
This tutorial explains how to count word frequencies using Python's collections.Counter from static, random, or file‑based text sources and then visualize the results with the WordCloud library, covering code examples, parameter settings, and available colormaps.
This tutorial walks you through creating a Python-powered QQ Music scraper that retrieves song details, lyrics, and comments, generates a word‑cloud visualization, and packages the functionality into a menu‑driven command‑line tool, complete with code snippets and troubleshooting tips.
This tutorial explains how to install and use the Jieba segmentation library and the WordCloud package in Python to process Chinese text, customize dictionaries and stopwords, and generate visually appealing word cloud images based on a mask picture.
This tutorial shows how to log in to Douban with Python, scrape short comments for the animated film “Nezha”, process the text using jieba for Chinese word segmentation, and generate a visual word cloud that highlights the most frequent audience sentiments.
This tutorial walks you through extracting dynamic Maoyan review data for the hit animation "Ne Zha" using Python, parsing the JSON payload, storing it as JSON, and then visualizing ratings, gender distribution, city hotspots, and word‑cloud insights with pyecharts and wordcloud.
This tutorial walks you through installing the WordCloud library, resolving common Windows compilation errors, handling Chinese font encoding issues, and creating both basic and image‑masked word clouds in Python, complete with code examples and visual results.
This tutorial walks you through installing the WordCloud library, resolving the Microsoft Visual C++ compiler error, fixing Chinese font issues, and creating both basic and image‑masked word clouds with sample Python code.
This tutorial walks you through using Python's wordcloud and jieba libraries to process WeChat Moments data, generate frequency statistics, and produce attractive, shape‑based word cloud images, complete with code snippets and visual examples.
This tutorial guides programmers through installing required Python libraries, extracting keywords from One Piece song lyrics using jieba, and generating a Chopper‑shaped word cloud with the wordcloud package in a Jupyter notebook, complete with code examples and visual results.
This tutorial walks you through installing the WordCloud and jieba packages, resolving common Windows compilation errors, handling Chinese font encoding issues, and creating both basic and image‑masked word clouds with practical code examples and screenshots.
This tutorial demonstrates how to use Python's itchat library to extract WeChat friends' signatures, save them to a file, and then generate a word‑cloud visualization with custom background and font, guiding readers through each step from data collection to visual output.
This tutorial shows how to capture hot comments from NetEase Cloud Music using Python web‑scraping techniques, handle the required encryption parameters, and then transform the data into bar charts and word clouds with pyecharts and WordCloud for clear visual insight.
This tutorial walks through using Python 3.5 to fetch the latest movies from Douban, extract their IDs and titles, crawl user comments, clean the text with regular expressions, segment Chinese words using Jieba, remove stopwords, compute word frequencies, and finally generate a word‑cloud visualization of the reviews.
This tutorial demonstrates how to use the Python itchat library to log into WeChat, analyze friend demographics, generate signature word clouds, and build an automatic reply bot, providing step‑by‑step code, required packages, and visual results.
This tutorial shows how to use the Python libraries jieba and wordcloud to quickly generate Chinese word‑cloud visualizations, explaining required dependencies, code structure, and usage tips so readers can produce word clouds from any Chinese text in just minutes.
This guide walks you through installing the Python WordCloud library, resolving common compilation errors, handling Chinese font encoding issues, and creating basic and image‑masked word clouds, complete with code snippets and troubleshooting tips for smooth visualization of Chinese text data.
