  Topic modeling is used for discovering latent semantic structure, usually
referred to as topics, in a large collection of documents. The most widely used
methods are Latent Dirichlet Allocation and Probabilistic Latent Semantic
Analysis. Despite their popularity they have several weaknesses. In order to
achieve optimal results they often require the number of topics to be known,
custom stop-word lists, stemming, and lemmatization. Additionally these methods
rely on bag-of-words representation of documents which ignore the ordering and
semantics of words. Distributed representations of documents and words have
gained popularity due to their ability to capture semantics of words and
documents. We present $\texttt{top2vec}$, which leverages joint document and
word semantic embedding to find $\textit{topic vectors}$. This model does not
require stop-word lists, stemming or lemmatization, and it automatically finds
the number of topics. The resulting topic vectors are jointly embedded with the
document and word vectors with distance between them representing semantic
similarity. Our experiments demonstrate that $\texttt{top2vec}$ finds topics
which are significantly more informative and representative of the corpus
trained on than probabilistic generative models.
