What is Latent Semantic Analysis (LSA)?
Latent Semantic Analysis (LSA) is a method used in natural language processing and information retrieval to analyze relationships between words and documents in a large corpus by reducing the dimensionality of the data. LSA is based on the assumption that words that are used in similar contexts have related meanings. It uses singular value decomposition (SVD), a matrix factorization technique, to.identify latent semantic structures in the data.
How does LSA work?
LSA works by performing the following steps:
- Create a term-document matrix, where each row represents a word and each column represents a document. The matrix elements are the frequency counts of the words in the documents.
- Apply singular value decomposition (SVD) to the term-document matrix, decomposing it into three matrices: U, S, and V^T.
- Reduce the dimensionality of the data by selecting the top k singular values in the S matrix and the corresponding columns in the U and V^T matrices.
- The reduced U and V^T matrices can be used to represent words and documents, respectively, in the lower-dimensional latent semantic space.
Example of LSA in Python using Scikit-learn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD
# Prepare a list of documents
documents = ['This is a sample document.', 'Another document in the corpus.', 'A third example document.']
# Create a TF-IDF vectorizer and fit it to the documents
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(documents)
# Perform Latent Semantic Analysis with 2 components
lsa = TruncatedSVD(n_components=2)
lsa.fit(X)
# Project the documents into the latent semantic space
document_vectors = lsa.transform(X)
print(document_vectors)
