Sage-Tips

Can LDA be used for topic Modelling?

Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions.

What are the limitations of LDA?

Common LDA limitations:

• Fixed K (the number of topics is fixed and must be known ahead of time)
• Uncorrelated topics (Dirichlet topic distribution cannot capture correlations)
• Non-hierarchical (in data-limited regimes hierarchical models allow sharing of data)
• Static (no evolution of topics over time)

Is NMF probabilistic?

Although KL-divergence based NMF inherits probabilistic meaning of topic model, the corresponding algorithms are typically much slower than those for standard NMF (Xie, Song, and Park 2013).

What is LDA Modelling?

In natural language processing, the latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.

Why is LDA better?

Basically LDA finds a centroid of each class datapoints. Whereas LDA works better with large dataset having multiple classes; class separability is an important factor while reducing dimensionality.

Why NMF is non-negative?

Almost all NMF algorithms use a two-block coordinate descent scheme (exact or inexact), that is, they optimize alternatively over one of the two factors, W or H, while keeping the other fixed. The reason is that the subproblem in one factor is convex. More precisely, it is a nonnegative least squares problem (NNLS).

What are the pros and cons of LDA and NMF?

There are pros and cons of both techniques. LDA is good in identifying coherent topics where as NMF usually gives incoherent topics. However, in the average case NMF and LDA are similar but LDA is more consistent.

What are the different approaches for topic modeling?

In this post, we will walk through two different approaches for topic modeling, and compare their results. These approaches are LDA (Latent Derilicht Analysis), and NMF (Non-negative Matrix factorization). Let’s talk about each of these before we move onto code.

What are LDA and LDA?

We will look at their definitions, and some basic math that describe how they work. LDA, or Latent Derelicht Analysis is a probabilistic model, and to obtain cluster assignments, it uses two probability values: P ( word | topics) and P ( topics | documents).

What is NMF non-negative matrix factorization?

NMF Non-Negative Matrix Factorization (NMF) is an unsupervised technique so there are no labeling of topics that the model will be trained on. The way it works is that, NMF decomposes (or factorizes) high-dimensional vectors into a lower-dimensional representation.