**What is Top2Vec?** - **Purpose:** Top2Vec is a topic modeling technique designed to discover hidden ("latent") topics within a large collection of text documents. It aims to overcome several shortcomings of traditional topic modeling methods like LDA and PLSA. - **Approach:** Top2Vec leverages the power of distributed representations (i.e., word and document embeddings) to capture the meaning of words and documents. This allows it to understand the semantic context in which words appear, going beyond simple word counts. - **Key Advantages:** - **No Need for Preprocessing:** Top2Vec doesn't require you to know the number of topics beforehand, create custom stop-word lists, or perform stemming/lemmatization. This reduces manual effort. - **Semantic Understanding:** It captures meaning within documents, considering word order and context rather than just word frequencies (bag-of-words). - **Integrated Representations:** Topic vectors are embedded in the same space as document and word vectors. This allows for direct comparisons to reveal semantic similarity. - **Results:** Experiments cited in the description suggest that Top2Vec produces more informative and representative topics compared to classic probabilistic topic modeling methods. # References ```dataview Table title as Title, authors as Authors where contains(subject, "Top2Vec") sort modified desc, authors, title ```