what is topic modelling?
Topic modeling is a type of statistical modeling that uses unsupervised Machine Learning to identify clusters or groups of similar words within a body of text. Topic modeling analyzes documents to identify common themes and provide an adequate cluster. In Natural Language Processing, Topic Modeling identifies and extracts abstract topics from large collections of text documents. The main models for analyzing topic modelling are Latent semantic analysis and latent Dirichlet.
latent semantic analysis
LSA is based on the principle that words that are close in meaning tend to be used together in context. LSA links words semantically by context and word frequency. It automatically creates separate topics based on previous inputs and outputs. It assumes all similar documents to share the same patterns when their word frequency and order are consistent. It relates closely to learning and understanding human language and judgment.
Latent Dirichlet Allocation
Topic modelling uses algorithms such as Latent Dirichlet Allocation (LDA) to identify latent topics in the text and represent documents as a mixture of these topics . Latent Dirichlet analysis analyzes large text files to categorize topics, provide valuable insights, and support better decision-making.
The German mathematician, Peter Gustav Lejeune Dirichlet came up with, Dirichlet processes, which in probability theory are “a family of stochastic processes whose realizations are probability distributions.”
Dirichlet model describes the pattern of the words that are repeating together, occurring frequently, and these words are similar to each other. This stochastic process uses Bayesian inferences for explaining “the prior knowledge about the distribution of random variables”. Estimating what are the chances of the words, which are spread over the document occurring again. The model builds data points, estimate probabilities making LDA a product of generative probabilistic model.
The LDA makes two key assumptions:
- Documents are a mixture of topics, and
- Topics are a mixture of tokens (or words)
In statistics , the documents are known as the probability density (or distribution) of topics and the topics are the probability density (or distribution) of words.
Topic modeling applications
**Document classification
**As an unsupervised Machine Learning technique that uses Natural Language Processing to understand the context and label new documents. It automatically tags each document with the topic it most closely resembles.
Analyzing customer feedback
With so many reviews analyzing customer feedback can be cumbersome due to a lack of understanding of the manual work involved. Sifting through huge quantities of feedbacks can also be costly and time consuming. With topic models, you can evaluate customer feedback.
Customer feedback is assessed and labels created based on what customers say.
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