how does topic modeling work

 


Two types of Topic Modeling Algorithms?

There are several algorithms for doing topic modeling. The most popular ones include:


  • Latent Semantic Analysis(LSA)

Latent Semantic Analysis, or LSA, is one of the crucial foundation techniques in topic modeling. We can use it for text summarization, text classification, and dimension reduction. It is similar to the cosine similarity. As for LSA, we develop a matrix using the words present in the document’s paragraphs in the corpus. The matrix rows will represent the unique words present in each section, and columns represent each paragraph.


  • Latent Dirichlet Allocation (LDA)

The Latent Dirichlet Allocation (LDA) & LSA are based on the same underlying assumptions: the distributional hypothesis, (i.e. similar topic makes use of similar words) and the statistical mixture hypothesis (i.e. documents talks about several topics) for which a statistical distribution can be determined.


The motive of LDA is to map each document in our corpus to a set of topics that covers a good deal of the words in the document.

How Does Topic Modeling Work?

Topic Modeling involves counting words and grouping similar word patterns to infer topics within unstructured data. So let’s say you're a software company and want to know what customers are saying about particular features of your product. So instead of spending hours & hours going through heaps of feedback, in an attempt to conclude which texts are talking about your topics of interest, you could analyze them with a topic modeling algorithm.


By detecting the patterns like word frequency and distance between words, a topic model clusters feedback that is similar. This also applies to words, phrases, and expressions that appear most frequently. And, with this information, you can instantly deduce what each set of texts are talking about.

Location : United States
Web : https://www.textrics.ai/

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