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

Описание: 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