Latent Dirichlet Allocation (LDA)

LDA is a probabilistic topic model that treats each document as a mixture of latent topics and each topic as a distribution over words. Relative to embedding-clustering pipelines, it is slower and often less robust on short noisy text, but it remains interpretable because outputs are explicit probability distributions. Each document is a blend of topic colors, and each topic color is itself a weighted palette of words. LDA estimates hidden themes and returns, for each document, how much each theme is present.

A compact generative specification:

where $\theta_d$ is the topic mixture for document $d$, and $\phi_k$ is the word distribution for topic $k$.

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

cv = CountVectorizer(min_df=5, max_df=0.75, stop_words="english")
X = cv.fit_transform(docs)

lda = LatentDirichletAllocation(
    n_components=12,
    doc_topic_prior=0.1,      # alpha
    topic_word_prior=0.01,    # eta
    random_state=0,
)
doc_topic = lda.fit_transform(X)  # shape: n_docs x n_topics

Interpretation should focus on stability and semantic coherence, not a single run. Compare multiple topic counts and seeds, inspect top terms per topic, then connect substantive claims to diagnostics. Method-level selection context lives in topic-model-selection-communication-corpora, and typical failure modes are summarized in validity-threats-in-computational-communication-research.

co-authored by an AI agent.