GitHub - ddangelov/Top2Vec: Top2Vec learns jointly embedded topic, document and word vectors.
Top2Vec is an algorithm for topic modeling and semantic search. It automatically detects topics present in text and generates jointly embedded topic, document and word vectors. Once you train the Top2Vec model you can:
The assumption the algorithm makes is that many semantically similar documents are indicative of an underlying topic. The first step is to create a joint embedding of document and word vectors. Once documents and words are embedded in a vector space the goal of the algorithm is to find dense clusters of documents, then identify which words attracted those documents together. Each dense area is a topic and the words that attracted the documents to the dense area are the topic words.
Document vectors in high dimensional space are very sparse, dimension reduction helps for finding dense areas. Each point is a document vector.
The red points are outlier documents and do not get used for calculating the topic vector. The purple points are the document vectors that belong to a dense area, from which the topic vector is calculated.
