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 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 demonstrates how to use Python for end‑to‑end text data analysis, covering preprocessing, word‑frequency visualization, classification, sentiment detection, similarity measurement, entity recognition, keyword extraction, summarization, translation, and generation with clear code examples.
This guide walks through ten core data preprocessing methods—including handling missing values, type conversion, standardization, encoding, smoothing, outlier treatment, text cleaning, word‑frequency counting, sentiment analysis, and topic modeling—each illustrated with concise Python code examples.
On January 17, the China Academy of Information and Communications Technology announced that Ant Security's self‑developed Tianjian multimodal content risk control system achieved the highest five‑star rating in both text and image assessments of the 2022 content review service evaluation, highlighting its advanced AI‑driven moderation capabilities.
This article explains how to perform Chinese part-of-speech tagging using the jieba.posseg library in Python, including loading stop words, extracting article content via Newspaper3k, applying precise mode segmentation, filtering, and presenting results in a pandas DataFrame.
This article explains how Elasticsearch uses Analyzer components—character filters, tokenizers, and token filters—to perform text analysis, reviews the built‑in analyzers such as standard, simple, stop, whitespace, keyword, pattern, language, ICU and IK, and provides practical _analyze API examples with code snippets and result screenshots.
This article details the business background, technical architecture, and practical deployments of natural language processing for content mining at Yuewen, covering tag construction, knowledge‑graph building, role analysis, recommendation generation, porn and plagiarism detection, and summarizing lessons learned.
This article explains the core components of Elasticsearch's full‑text search analysis—Analyzers, Tokenizers, and Token Filters—detailing their roles, building blocks, built‑in types, and how they combine to customize text processing for effective indexing and querying.
