Abstract
Topic modeling refers to a range of algorithms in natural language processing that gives us an insight into the ‘latent’ semantic topics or patterns in a collection of documents. These patterns of word co-occurrence are used to determine the hidden ‘topics’ which are present in the corpus. Topic modeling has been used successfully for information retrieval, classifying documents, summarizing them and for exploratory analysis of large corpora of texts. This survey studies various algorithms that have been used for topic modeling over time including TF-IDF, latent Dirichlet algorithm (LDA), clustering on sentence-level BERT embeddings and a newer hybrid approach of generating contextual topics using a combination of LDA and BERT vectors. This survey will analyze the advantages and limitations of these algorithms.KeywordsNatural language processingTopic modelingTF-IDFSentence-BERTLatent Dirichlet algorithmContextual topic analysis
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