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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.
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 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.
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.
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 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 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 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.
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.