LDA is a generative probabilistic model that assumes each document is a mixture of a small number of topics and that each word in the document is attributable to one of the document’s topics. It uses Dirichlet priors for the document-topic and topic-word distributions.
#How it works
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Initialization:
- Choose the number of topics $k$.
- Initialize topic distributions for each document and word distributions for each topic.
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Iterative Process:
- For each document, randomly assign each word to one of the $k$ topics.
- Iteratively update the topic assignments for each word based on:
- The prevalence of topics in the document.
- The prevalence of words in the topic.
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Convergence:
- The process continues until the topic assignments stabilize, meaning that further iterations do not significantly change the topic distributions.
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Topic Interpretation:
- After convergence, each document is represented as a distribution over topics.
- Each topic is represented as a distribution over words.
#Advantages
- Produces interpretable topics
- Captures the probabilistic nature of topic distributions
#Limitations
- Requires number of topics to be specified in advance.
- Computationally intensive for large datasets
#Comparison with LSA
- LSA is based on linear algebra and dimensionality reduction, while LDA is based on probabilistic modeling.
- LSA is simpler and faster but less interpretable, whereas LDA provides more interpretable topics but is computationally more demanding.
- LSA can handle synonymy and polysemy to some extent, but LDA explicitly models the generative process of documents, making it more robust in capturing the underlying topic structure.
Laplace Smoothing