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 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 demonstrates how to collect Douyin video comments using Python, perform exploratory data analysis, and apply LDA topic modeling to uncover the main themes and user interests hidden in thousands of short comments.
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.
The presentation explains how JD.com leverages large‑scale text mining and NLP techniques—including data cleaning, multi‑level labeling, sentiment classification with models such as TextCNN, RoBERTa, and USE—to transform unstructured customer feedback into actionable product insights across various e‑commerce scenarios.
This article presents practical experiences in post‑loan management, detailing how to extract descriptive and deep‑learning features from voice recordings and textual transcripts, apply traditional signal processing, keyword and TF‑IDF methods, and build CRNN and transformer models to predict repayment behavior.
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.
At Baidu Search, a large‑scale short‑text clustering system was built using multi‑level semantic splitting, fine‑grained aggregation and error‑correction, evolving from v1.0 to v2.0, and now clusters 100 million queries with 95 % accuracy and 80 % recall within three days.
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 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.
This article describes how advanced natural‑language‑processing, big‑data, and deep‑learning techniques are integrated into an end‑to‑end platform for financial asset management, covering large‑scale bid‑tender text analysis, few‑shot sentiment monitoring, model architectures, data‑enhancement methods, and practical deployment results.
Using Python to scrape 150,000 NetEase Cloud Music folk lyrics, the author applies jieba segmentation, word‑frequency analysis, word‑cloud visualization, seasonal and geographic preferences, and SnowNLP sentiment scoring to reveal the themes, hopes, and city loves of Chinese folk singers.
This article explains how Beike, China’s largest residential service platform, leverages its massive house, client, and text data to design a multi‑layered tag architecture, detailing data sources, tag construction methods—including classification, keyword, geographic, anonymous topic, and temporal tags—and their application to improve personalized house search and recommendation.
This article outlines the end‑to‑end text‑mining workflow—from data acquisition and preprocessing to feature extraction, algorithm selection, and model evaluation—while demonstrating a sentiment‑analysis case study that combines LDA topic modeling with deep‑learning classifiers.
The article examines how telemarketing, a data‑intensive industry, leverages various data‑mining methods—including supervised classification, clustering, operations research optimization, anomaly detection, and text mining—to improve lead selection, agent allocation, churn prediction, and voice analysis, while also outlining the key data‑talent roles needed for successful implementation.
This article explains a flexible template‑matching approach that dynamically extracts concise, user‑friendly tags from online travel reviews, detailing the system architecture, key concepts, step‑by‑step implementation, and matching rules that improve recall and relevance.
Vipshop's in‑house sentiment monitoring platform integrates web‑scraped reviews, WeChat comments and internal service messages, applying lexical sentiment scoring, dictionary‑based Chinese word segmentation, TF‑IDF keyword ranking and lightweight classification to deliver real‑time insights, alerts and actionable reports for thousands of daily user comments.
iQIYI’s NLP pipeline—combining CRF‑based segmentation, bidirectional LSTM/GRU models with attention and a CNN classifier—automatically extracts opinion targets, sentiment words and polarity from unstructured video comments, aggregates them across users to reveal collective attitudes toward actors, plot, and visual effects, and guides future work on implicit opinions and broader sentiment domains.
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 article explains how to apply TF‑IDF weighting and the K‑means clustering algorithm in Python to identify a small set of representative regression cases from hundreds of thousands of production request records, including guidance on selecting the optimal number of clusters.
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 presents a comprehensive NLP‑driven text‑mining workflow for tourism comment data, covering data cleaning, word2vec training, keyword extraction, sentiment analysis, ranking, and a lightweight architecture that enables fast, accurate recommendation of scenic spots for users.
The talk demonstrates how Meipai, a leading Chinese short‑video community, leverages large‑scale text mining and machine‑learning techniques—ranging from anti‑spam filtering to AI‑enhanced search—to enrich captions, comments, and metadata, improve user experience, and inspire further research on text data in video platforms.
The fourth Meitu Internet Technology Salon showcased practical AI and machine learning uses, highlighting Meipai’s text‑anti‑spam, hot‑topic detection, sentiment analysis and personalized video search, while Baidu demonstrated ML‑driven business intelligence tools for multi‑source data mining, user profiling, and intelligent enterprise and HR management.
This article examines the rapid growth of structured and unstructured data in the securities sector, outlines text mining fundamentals, explores key algorithms and tools, and analyzes current industry services, investment communities, and professional solutions while highlighting existing challenges and future opportunities.
This article details how Industrial and Commercial Bank of China (ICBC) applies text mining and natural language processing to analyze both internal call‑center records and external online discussions, building ontologies and models that turn massive unstructured feedback into actionable insights for improving service quality and reducing costs.
