LDA Topic Modeling

Latent Dirichlet allocation (LDA) is a probabilistic topic model that represents each document as a mixture of topics and each topic as a distribution over words.

잠재 디리클레 할당(LDA)은 각 문서를 주제들의 혼합으로, 각 주제를 단어들의 분포로 나타내는 확률적 토픽 모형이다.

How do I discover the main themes in a large collection of texts?

대량의 텍스트에서 주요 주제를 어떻게 찾아내나요?

LDA topic modeling infers latent topics from word co-occurrence patterns, giving each document a topic proportion and each topic a list of characteristic words. It fits large collections of documents such as news articles, abstracts or open-ended responses, with the number of topics chosen using coherence and interpretability.

When to use LDA Topic Modeling

• Large collections of news articles, abstracts or reviews
• Exploring themes without predefined categories
• Tracking topic prevalence over time or across groups

When to use something else

• Texts are very short, such as tweets, and semantic similarity matters: use BERTopic or embedding-based clustering
• Categories are predefined and labeled examples exist: use supervised text classification

Assumptions and requirements

• Preprocessed text (tokenization, stop-word removal, lemmatization)
• A chosen number of topics
• Bag-of-words representation (word order ignored)
• Enough documents and words per document for stable topics

What the analysis reports

• Top words per topic
• Document–topic proportions
• Coherence scores across numbers of topics
• Topic labels and prevalence
• Intertopic distance visualization

How to report it

An LDA model with [k] topics was selected based on coherence (C_v = [value]), and the most prevalent topic, [label], accounted for [value]% of the corpus.

Related methods

• BERTopic
• Non Negative Matrix Factorization (NMF)
• Structural Topic Model

Run LDA Topic Modeling on Quorant