Topic Modeling

Topic modeling is an unsupervised NLP technique that discovers latent themes (topics) across a collection of documents. It treats each document as a mixture of topics and each topic as a distribution over words. The most common algorithm is Latent Dirichlet Allocation (LDA), which outputs a set of topic-word distributions and document-topic proportions. Operators encounter topic modeling when analyzing large text corpora (e.g., chat logs, support tickets) to identify recurring themes without manual labeling. It's distinct from classification: topics emerge from the data, not from predefined labels.

LDA assumes documents are generated by first picking a distribution over topics (Dirichlet prior), then for each word picking a topic from that distribution, and finally picking a word from the topic's word distribution. Training uses variational inference or Gibbs sampling to reverse this process. The key hyperparameters are the number of topics (K) and the Dirichlet priors (alpha, beta). In practice, operators must preprocess text (lowercasing, removing stopwords, stemming/lemmatization) and tune K using coherence scores (e.g., C_v). Topic modeling is sensitive to corpus size: small corpora (<1000 docs) produce unstable topics. Modern alternatives include BERTopic, which uses sentence embeddings and clustering, often yielding more coherent topics than LDA.

An operator analyzing 10,000 support tickets runs from gensim.models import LdaModel on preprocessed text. Setting num_topics=10 and passes=20 yields topics like ['password','reset','login'] (Topic 0) and ['refund','cancel','order'] (Topic 1). Coherence score C_v=0.45 indicates moderate quality; retraining with num_topics=15 raises it to 0.52. On a 16 GB RAM machine, training takes ~2 minutes.

In a Python script using Hugging Face Transformers, an operator loads a corpus, tokenizes with bert-base-uncased, then applies BERTopic: from bertopic import BERTopic; model = BERTopic(); topics, probs = model.fit_transform(docs). The model outputs a list of topic assignments per document and a visualization of topic clusters. For LDA with Gensim: from gensim.corpora import Dictionary; dictionary = Dictionary(docs); corpus = [dictionary.doc2bow(doc) for doc in docs]; lda = LdaModel(corpus, num_topics=10, id2word=dictionary, passes=20).