NLP- Lesson 2 - Part 2 - Topic Modeling with (NMF & SVD)
Topic Modeling with NMF and SVD: A Beginner's Guide
Learn the basics of topic modeling with non-negative matrix factorization (NMF) and singular value decomposition (SVD) in this video tutorial. We'll also cover TF-IDF, truncated SVD, and timing comparisons.
Topic modeling is a machine learning technique that discovers abstract topics in a collection of documents. It's used in a variety of applications, including text classification, document clustering, and recommendation systems.
Non-negative matrix factorization (NMF) and singular value decomposition (SVD) are two popular matrix decomposition techniques that can be used for topic modeling. NMF is a non-exact factorization that produces non-negative matrices, which can be easier to interpret than the matrices produced by SVD. However, SVD is an exact factorization and can be faster to compute than NMF.
TF-IDF stands for term frequency-inverse document frequency. It's a measure of the importance of a word in a document, relative to the rest of the corpus. TF-IDF is often used as a pre-processing step for topic modeling, as it can help to identify the most important words in the corpus.
Truncated SVD is a variation of SVD that keeps only the top k singular values. This can be used to reduce the dimensionality of the data, which can improve the performance of topic modeling algorithms.
Timing comparisons show that NMF is generally slower than SVD. However, the difference in speed can vary depending on the size and complexity of the dataset.
In this video, we'll cover the following topics:
What is topic modeling?
What are NMF and SVD?
How to use NMF and SVD for topic modeling
What is TF-IDF and why is it used for topic modeling?
What is truncated SVD and why is it used for topic modeling?
Timing comparisons of NMF and SVD
Watch this video to learn Topic modeling with NMF and SVD.
Keywords: Topic modeling, NMF, SVD, TF-IDF, truncated SVD, machine learning, text classification, document clustering, recommendation systems