__Techniques for Topic Modeling __

**Topic modeling is regarding logically
correlating many words. Say a medium operator desires to spot whether or not
the poor network may be a reason for low client satisfaction. Here, “bad
network” is that the topic. The document is analyzed for words like “bad”,
“slow speed”, “call not connecting”, etc., that are additional doubtless to
explain network problems compared to common words like “the” or “and”. **

** **

* Latent linguistics Analysis (LSA) *

*Latent linguistics Analysis (LSA)*

**Latent linguistics analysis (LSA) aims to
leverage the context around the words so as to capture hidden ideas or
topics. **

** **

**In this technique, machines use Term
Frequency-Inverse Document Frequency (TF-IDF) for analyzing documents. TF-IDF
may be a numerical data point that reflects however necessary a word is to a document
at intervals a corpus. **

** **

**Say there's a set of ‘m’ text documents and
every document incorporates a total of ‘n’ distinctive words. The TF-IDF matrix
– m*n – contains the TF-IDF scores for every word within the document. This
matrix is then reduced to ‘k's dimensions, k being the required variety of
topics. The reduction is finished victimization Singular price Decomposition
(SVD). **

**This decomposition provides a vector
illustration of every|of every} word term in each document within the entire
assortment through the equation A = USVT where:
**

** **

**A is that the SVD matrix **

**U is that the vector illustration of the
documents with vector length k **

**V is that the vector illustration of
terms within the given document with length k **

**S represents the square matrix of the
singular topic frequency scores **

**T may be a hyperparameter reflective of
the number of topics **

**The SVD matrix will be wont to notice
similar topics and documents victimization of the trigonometric function
similarity technique. **

**The main disadvantages of LSA are the
inefficient illustration and non-interpretable embeddings. It additionally
needs an oversized corpus to yield correct results.**