What is Latent Dirichlet Allocation (LDA)?
Latent Dirichlet Allocation (LDA) is a generative probabilistic model used in natural language processing and machine learning for discovering topics in large collections of documents. LDA assumes that documents are mixtures of topics, and topics are probability distributions over words. Given a collection of documents, LDA aims to learn the latent topic structure and the topic distributions for each document.
How does LDA work?
LDA works by performing the following steps:
- Choose the number of topics, K.
- Randomly assign each word in each document to one of the K topics.
- For each document, update the topic assignments for its words by considering the topic distribution in the document and the word distribution in the topics.
- Iterate the previous step until convergence, at which point the algorithm has learned the topic structure and the topic distributions for the documents.
Example of LDA in Python using Gensim
from gensim.corpora import Dictionary
from gensim.models import LdaModel
# Create a corpus from a list of texts
texts = [['word1', 'word2', 'word3'], ['word2', 'word3', 'word4'], ['word1', 'word2', 'word4']]
dictionary = Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
# Train the LDA model
num_topics = 2
lda_model = LdaModel(corpus, num_topics=num_topics, id2word=dictionary, passes=10)
# Print the discovered topics
for topic_id in range(num_topics):
print(f"Topic {topic_id}: {lda_model.print_topic(topic_id)}")
