BERTopic Tutorial in 10 Mins | Topic Modeling Applied in Financial News Analysis скачать в хорошем качестве

BERTopic Tutorial in 10 Mins | Topic Modeling Applied in Financial News Analysis

Unlock the power of topic modeling in financial news with this BERTopic tutorial. In this video, you’ll learn how to apply financial news topic modeling using BERTopic, a popular Python library designed to transform unstructured news articles into actionable insights. We break down BERTopic’s six core modules: Embeddings: Convert financial news into vector representations using transformer-based models for rich semantic understanding. Dimensionality Reduction: Apply techniques like UMAP and PCA to simplify embeddings while retaining important relationships. Clustering: Use algorithms such as HDBSCAN and K-Means to group similar news stories into trending topics. Vectorizers: Turn clustered text into numerical features for precise topic analysis. c-TF-IDF: Score key terms within and across topics to identify the most important words in your financial news dataset. Representation Model: Refine topic keywords with semantic similarity techniques like KeyBERT, making topics more meaningful and actionable. We’ll walk through practical examples using real Apple stock market news data, giving you hands-on experience in extracting and visualizing topics from financial news. 🗒️ Articles mentioned in the video: https://towardsdatascience.com/a-prac... ☕ Stay Connected: Support our channel: https://buymeacoffee.com/visualdesign Website: https://www.visual-design.net/ Medium:   / destingong