文本挖掘
Text Mining and Computational Text Analysis
This course introduces text preprocessing, keyword extraction, topic modeling, text classification, sentiment analysis, and LLM-assisted evidence extraction. It is designed for research tasks involving academic literature, policy documents, patents, online communities, and business texts.
Course Introduction
文本挖掘的核心目标，是把非结构化文本转化为可以分析、解释和建模的信息资源。它既可以服务于关键词识别、主题发现和情感分析，也可以进一步支持知识发现、文献综述、政策分析和用户画像构建。
This course connects technical procedures with research design. It pays attention not only to algorithms, but also to how textual evidence can support a convincing academic argument.
Course Directory
Click a section to open the Markdown-based teaching document.
Text Data and Preprocessing
Learn how to transform raw textual materials into analyzable research data.
Topic Modeling and Text Classification
Understand classical and neural approaches to extracting semantic structures from texts.
Research Design with Text Mining
Apply text mining to literature review, policy analysis, patent intelligence, and online community research.